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""" |
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Enhanced Product Feature Ideation & Validation Agent |
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Improved Gradio UI with better UX and visualizations |
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""" |
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import gradio as gr |
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import asyncio |
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import json |
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from typing import Dict, List, Any |
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import pandas as pd |
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from datetime import datetime |
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import logging |
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import os |
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from dotenv import load_dotenv |
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import plotly.express as px |
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import plotly.graph_objects as go |
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import traceback |
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load_dotenv() |
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logging.basicConfig(level=logging.INFO) |
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logger = logging.getLogger(__name__) |
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from data_collectors.iykyk_app_store_collector import IYKYKAppStoreCollector |
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from data_collectors.reddit_collector import RedditCollector |
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from data_collectors.news_collector import NewsCollector |
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from data_collectors.arxiv_collector import ArxivCollector |
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from data_collectors.linkedin_collector import LinkedInCollector |
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from analyzers.sentiment_analyzer import SentimentAnalyzer |
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from analyzers.feature_validator import FeatureValidator |
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from modal_functions.gpu_processing import GPUProcessor |
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class EnhancedProductFeatureAgent: |
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"""Enhanced agent class with better UI integration""" |
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def __init__(self): |
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self.app_store = IYKYKAppStoreCollector() |
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self.reddit = RedditCollector() |
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self.news = NewsCollector() |
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self.arxiv = ArxivCollector() |
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self.linkedin = LinkedInCollector() |
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self.sentiment = SentimentAnalyzer() |
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self.validator = FeatureValidator() |
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self.gpu_processor = GPUProcessor() |
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self.validation_history = [] |
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async def validate_feature_enhanced(self, feature_description: str, target_market: str, |
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competitor_apps: str, budget: float, timeline: str, |
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include_academic: bool = True, geographic_focus: str = "Global", |
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reddit_weight: float = 1.0, news_weight: float = 1.0, |
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app_store_weight: float = 1.0) -> Dict[str, Any]: |
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"""Enhanced validation with additional parameters""" |
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try: |
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logger.info(f"Starting enhanced validation for feature: {feature_description}") |
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competitor_list = [app.strip() for app in competitor_apps.split(",")] |
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tasks = [ |
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self.app_store.get_competitor_reviews(competitor_list), |
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self.reddit.get_market_sentiment(feature_description, target_market), |
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self.news.get_trend_analysis(feature_description), |
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self.linkedin.analyze_professional_insights(feature_description, target_market), |
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] |
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if include_academic: |
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tasks.append(self.arxiv.check_novelty(feature_description)) |
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results = await asyncio.gather(*tasks) |
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app_reviews, reddit_data, news_trends, linkedin_data = results[:4] |
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arxiv_research = results[4] if include_academic else {} |
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logger.info("Starting GPU-accelerated sentiment analysis...") |
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sentiment_results = await self.gpu_processor.batch_sentiment_analysis([ |
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app_reviews, reddit_data, news_trends |
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]) |
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validation_score = self.validator.calculate_validation_score( |
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sentiment_results, feature_description, target_market, |
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reddit_weight, news_weight, app_store_weight |
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) |
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data_stats = self._calculate_enhanced_statistics(app_reviews, reddit_data, news_trends, linkedin_data, arxiv_research) |
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report = { |
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"feature_description": feature_description, |
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"target_market": target_market, |
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"budget": budget, |
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"timeline": timeline, |
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"geographic_focus": geographic_focus, |
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"validation_score": validation_score, |
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"data_collection_stats": data_stats, |
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"market_trends": news_trends, |
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"competitor_insights": app_reviews, |
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"reddit_sentiment": reddit_data, |
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"academic_research": arxiv_research if include_academic else None, |
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"recommendations": self._generate_enhanced_recommendations( |
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validation_score, app_reviews, reddit_data, news_trends, budget, timeline |
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), |
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"risk_assessment": self._assess_risks(validation_score, data_stats), |
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"timestamp": datetime.now().isoformat() |
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} |
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self.validation_history.append({ |
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"feature": feature_description, |
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"score": validation_score, |
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"timestamp": datetime.now().isoformat() |
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}) |
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logger.info(f"Enhanced validation completed with score: {validation_score:.2f}") |
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return report |
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except Exception as e: |
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logger.error(f"Error in enhanced feature validation: {str(e)}") |
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traceback.print_exc() |
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return {"error": str(e)} |
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finally: |
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try: |
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await self.reddit.cleanup() |
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except Exception as cleanup_error: |
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logger.debug(f"Reddit cleanup error: {str(cleanup_error)}") |
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def _calculate_enhanced_statistics(self, app_reviews, reddit_data, news_trends, linkedin_data, arxiv_research): |
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"""Calculate comprehensive statistics for all 5 data sources""" |
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stats = { |
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"total_data_points": 0, |
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"data_quality_score": 0, |
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"sources": { |
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"app_store": { |
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"status": "unknown", |
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"data_points": 0, |
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"quality": 0, |
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"apps_analyzed": 0, |
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"total_ratings": 0, |
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"average_rating": 0, |
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"summary": "" |
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}, |
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"reddit": { |
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"status": "unknown", |
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"data_points": 0, |
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"quality": 0, |
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"posts_found": 0, |
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"subreddits_searched": 0, |
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"sentiment_distribution": {}, |
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"summary": "" |
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}, |
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"news": { |
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"status": "unknown", |
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"data_points": 0, |
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"quality": 0, |
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"articles_found": 0, |
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"sources_searched": 0, |
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"trending_topics": [], |
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"summary": "" |
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}, |
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"linkedin": { |
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"status": "unknown", |
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"data_points": 0, |
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"quality": 0, |
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"posts_found": 0, |
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"professional_insights": 0, |
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"industry_sentiment": "", |
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"summary": "" |
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}, |
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"arxiv": { |
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"status": "unknown", |
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"data_points": 0, |
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"quality": 0, |
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"papers_found": 0, |
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"novelty_score": 0, |
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"research_areas": [], |
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"summary": "" |
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} |
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}, |
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"confidence_level": 0 |
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} |
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successful_sources = 0 |
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total_quality = 0 |
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if app_reviews and "error" not in app_reviews: |
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stats["sources"]["app_store"]["status"] = "success" |
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successful_sources += 1 |
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if "apps" in app_reviews: |
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apps_count = len(app_reviews["apps"]) |
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total_ratings = 0 |
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rating_sum = 0 |
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rating_count = 0 |
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for app_name, app_data in app_reviews["apps"].items(): |
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if "app_metadata" in app_data: |
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metadata = app_data["app_metadata"] |
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if "rating_count" in metadata: |
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total_ratings += metadata["rating_count"] |
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if "rating" in metadata and metadata["rating"] > 0: |
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rating_sum += metadata["rating"] |
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rating_count += 1 |
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stats["sources"]["app_store"]["apps_analyzed"] = apps_count |
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stats["sources"]["app_store"]["total_ratings"] = total_ratings |
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stats["sources"]["app_store"]["average_rating"] = round(rating_sum / rating_count, 2) if rating_count > 0 else 0 |
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stats["sources"]["app_store"]["data_points"] = total_ratings |
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stats["sources"]["app_store"]["quality"] = min(total_ratings / 10000, 1.0) |
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stats["sources"]["app_store"]["summary"] = f"Analyzed {apps_count} competitor apps with {total_ratings:,} total user ratings" |
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stats["total_data_points"] += total_ratings |
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total_quality += stats["sources"]["app_store"]["quality"] |
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else: |
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stats["sources"]["app_store"]["status"] = "failed" |
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stats["sources"]["app_store"]["summary"] = "App Store data collection failed" |
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if reddit_data and "error" not in reddit_data: |
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stats["sources"]["reddit"]["status"] = "success" |
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successful_sources += 1 |
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posts_count = 0 |
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subreddits = set() |
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sentiment_dist = {"positive": 0, "negative": 0, "neutral": 0} |
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if "query_results" in reddit_data: |
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for query, result in reddit_data["query_results"].items(): |
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if "posts" in result: |
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posts_count += len(result["posts"]) |
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if "subreddits" in result: |
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subreddits.update(result["subreddits"]) |
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if "aggregate_sentiment" in reddit_data: |
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sentiment_dist = reddit_data["aggregate_sentiment"].get("sentiment_distribution", sentiment_dist) |
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stats["sources"]["reddit"]["posts_found"] = posts_count |
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stats["sources"]["reddit"]["subreddits_searched"] = len(subreddits) |
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stats["sources"]["reddit"]["sentiment_distribution"] = sentiment_dist |
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stats["sources"]["reddit"]["data_points"] = posts_count |
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stats["sources"]["reddit"]["quality"] = min(posts_count / 50, 1.0) |
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dominant_sentiment = max(sentiment_dist.items(), key=lambda x: x[1])[0] if sentiment_dist else "neutral" |
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stats["sources"]["reddit"]["summary"] = f"Found {posts_count} posts across {len(subreddits)} subreddits with {dominant_sentiment} sentiment" |
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stats["total_data_points"] += posts_count |
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total_quality += stats["sources"]["reddit"]["quality"] |
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else: |
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stats["sources"]["reddit"]["status"] = "failed" |
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stats["sources"]["reddit"]["summary"] = "Reddit data collection failed" |
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if news_trends and "error" not in news_trends: |
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stats["sources"]["news"]["status"] = "success" |
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successful_sources += 1 |
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articles_count = 0 |
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sources_set = set() |
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if "query_results" in news_trends: |
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for query, result in news_trends["query_results"].items(): |
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if "articles" in result: |
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articles = result["articles"] |
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articles_count += len(articles) |
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for article in articles: |
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if "source" in article: |
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sources_set.add(article["source"]) |
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stats["sources"]["news"]["articles_found"] = articles_count |
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stats["sources"]["news"]["sources_searched"] = len(sources_set) |
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stats["sources"]["news"]["data_points"] = articles_count |
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stats["sources"]["news"]["quality"] = min(articles_count / 100, 1.0) |
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stats["sources"]["news"]["summary"] = f"Collected {articles_count} articles from {len(sources_set)} news sources" |
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stats["total_data_points"] += articles_count |
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total_quality += stats["sources"]["news"]["quality"] |
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else: |
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stats["sources"]["news"]["status"] = "failed" |
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stats["sources"]["news"]["summary"] = "News data collection failed" |
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if linkedin_data and "error" not in linkedin_data: |
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stats["sources"]["linkedin"]["status"] = "success" |
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successful_sources += 1 |
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posts_count = linkedin_data.get("total_posts", 0) |
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professional_insights = len(linkedin_data.get("professional_insights", {}).get("professional_recommendations", [])) |
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sentiment_analysis = linkedin_data.get("professional_insights", {}).get("sentiment_analysis", {}) |
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industry_sentiment = sentiment_analysis.get("dominant_sentiment", "neutral") |
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stats["sources"]["linkedin"]["posts_found"] = posts_count |
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stats["sources"]["linkedin"]["professional_insights"] = professional_insights |
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stats["sources"]["linkedin"]["industry_sentiment"] = industry_sentiment |
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stats["sources"]["linkedin"]["data_points"] = posts_count |
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stats["sources"]["linkedin"]["quality"] = min(posts_count / 20, 1.0) |
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stats["sources"]["linkedin"]["summary"] = f"Analyzed {posts_count} professional posts with {industry_sentiment} industry sentiment" |
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stats["total_data_points"] += posts_count |
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total_quality += stats["sources"]["linkedin"]["quality"] |
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else: |
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stats["sources"]["linkedin"]["status"] = "failed" |
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stats["sources"]["linkedin"]["summary"] = "LinkedIn data collection failed" |
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if arxiv_research and "error" not in arxiv_research: |
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stats["sources"]["arxiv"]["status"] = "success" |
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successful_sources += 1 |
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papers_count = len(arxiv_research.get("papers", [])) |
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novelty_score = arxiv_research.get("novelty_score", 0) |
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stats["sources"]["arxiv"]["papers_found"] = papers_count |
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stats["sources"]["arxiv"]["novelty_score"] = novelty_score |
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stats["sources"]["arxiv"]["data_points"] = papers_count |
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stats["sources"]["arxiv"]["quality"] = min(papers_count / 50, 1.0) |
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stats["sources"]["arxiv"]["summary"] = f"Found {papers_count} academic papers with novelty score {novelty_score:.1f}/10" |
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stats["total_data_points"] += papers_count |
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total_quality += stats["sources"]["arxiv"]["quality"] |
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else: |
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stats["sources"]["arxiv"]["status"] = "failed" |
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stats["sources"]["arxiv"]["summary"] = "ArXiv data collection failed" |
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total_sources = 5 |
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stats["data_quality_score"] = round(total_quality / total_sources, 2) if successful_sources > 0 else 0 |
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stats["confidence_level"] = round((successful_sources / total_sources) * stats["data_quality_score"], 2) |
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return stats |
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def _generate_enhanced_recommendations(self, score, app_reviews, reddit_data, news_trends, budget, timeline): |
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"""Generate enhanced recommendations based on all factors""" |
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recommendations = [] |
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if score >= 8: |
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recommendations.append("🟢 **Excellent validation score!** This feature shows strong market potential.") |
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recommendations.append("💡 Consider fast-tracking development to capture market opportunity.") |
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elif score >= 6: |
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recommendations.append("🟡 **Good validation score.** Feature shows promise with some considerations.") |
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recommendations.append("🔍 Review specific feedback areas for optimization opportunities.") |
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else: |
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recommendations.append("🔴 **Low validation score.** Significant market challenges identified.") |
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recommendations.append("⚠️ Consider pivoting or addressing core concerns before proceeding.") |
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if budget < 10000: |
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recommendations.append("💰 **Limited budget detected.** Focus on MVP features and lean development.") |
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elif budget > 50000: |
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recommendations.append("💰 **Substantial budget available.** Consider comprehensive feature set and quality assurance.") |
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if "1-3 months" in timeline: |
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recommendations.append("⏰ **Aggressive timeline.** Prioritize core features and consider phased rollout.") |
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elif "12+" in timeline: |
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recommendations.append("⏰ **Extended timeline.** Opportunity for thorough market research and iterative development.") |
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if reddit_data and reddit_data.get("aggregate_sentiment", {}).get("total_posts", 0) > 50: |
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recommendations.append("🗣️ **Strong social engagement detected.** Leverage community feedback for feature refinement.") |
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return "\n".join(f"- {rec}" for rec in recommendations) |
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def _assess_risks(self, score, data_stats): |
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"""Assess risks based on validation results""" |
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risks = [] |
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if score < 5: |
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risks.append("Low market validation score indicates significant market risk") |
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if data_stats["confidence_level"] < 0.5: |
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risks.append("Limited data availability reduces confidence in analysis") |
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if data_stats["sources"]["app_store"]["status"] == "failed": |
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risks.append("Unable to analyze competitor landscape effectively") |
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return risks |
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enhanced_agent = EnhancedProductFeatureAgent() |
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def create_score_visualization(score): |
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"""Create HTML visualization for validation score""" |
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if score >= 8: |
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color = "#28a745" |
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emoji = "🟢" |
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status = "Excellent" |
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bg_color = "#d4edda" |
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elif score >= 6: |
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color = "#ffc107" |
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emoji = "🟡" |
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status = "Good" |
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bg_color = "#fff3cd" |
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else: |
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color = "#dc3545" |
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emoji = "🔴" |
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status = "Needs Work" |
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bg_color = "#f8d7da" |
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html = f""" |
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<div style="text-align: center; padding: 20px; border-radius: 10px; background: {bg_color}; border: 2px solid {color}; margin: 10px 0;"> |
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<h2 style="color: {color}; margin: 0; font-size: 2.5em;">{emoji} {score:.1f}/10</h2> |
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<p style="color: {color}; margin: 5px 0 0 0; font-weight: bold; font-size: 1.2em;">{status}</p> |
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</div> |
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""" |
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|
return html |
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|
|
|
def create_data_quality_chart(data_stats): |
|
|
"""Create data quality visualization""" |
|
|
sources = data_stats.get("sources", {}) |
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|
|
|
source_names = [] |
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|
quality_scores = [] |
|
|
data_points = [] |
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for source, stats in sources.items(): |
|
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source_names.append(source.replace("_", " ").title()) |
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quality_scores.append(stats.get("quality", 0)) |
|
|
data_points.append(stats.get("data_points", 0)) |
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|
|
|
fig = go.Figure() |
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|
|
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fig.add_trace(go.Bar( |
|
|
name='Data Quality', |
|
|
x=source_names, |
|
|
y=quality_scores, |
|
|
yaxis='y', |
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offsetgroup=1, |
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marker_color='lightblue' |
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)) |
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|
|
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max_points = max(data_points) if data_points else 1 |
|
|
scaled_points = [p / max_points for p in data_points] |
|
|
|
|
|
fig.add_trace(go.Bar( |
|
|
name='Data Volume (scaled)', |
|
|
x=source_names, |
|
|
y=scaled_points, |
|
|
yaxis='y', |
|
|
offsetgroup=2, |
|
|
marker_color='lightgreen' |
|
|
)) |
|
|
|
|
|
fig.update_layout( |
|
|
title='Data Collection Quality by Source', |
|
|
xaxis_title='Data Sources', |
|
|
yaxis_title='Quality Score (0-1)', |
|
|
barmode='group', |
|
|
height=400 |
|
|
) |
|
|
|
|
|
return fig |
|
|
|
|
|
def enhanced_gradio_validate_feature(feature_desc, target_market, competitor_apps, budget, timeline, |
|
|
include_academic, geographic_focus, reddit_weight, news_weight, |
|
|
app_store_weight, progress=gr.Progress()): |
|
|
"""Enhanced Gradio wrapper with better progress tracking and outputs""" |
|
|
|
|
|
progress(0.1, desc="🚀 Initializing enhanced validation...") |
|
|
|
|
|
|
|
|
fresh_agent = EnhancedProductFeatureAgent() |
|
|
|
|
|
try: |
|
|
|
|
|
import concurrent.futures |
|
|
|
|
|
def run_validation(): |
|
|
new_loop = asyncio.new_event_loop() |
|
|
asyncio.set_event_loop(new_loop) |
|
|
try: |
|
|
return new_loop.run_until_complete( |
|
|
fresh_agent.validate_feature_enhanced( |
|
|
feature_desc, target_market, competitor_apps, budget, timeline, |
|
|
include_academic, geographic_focus, reddit_weight, news_weight, app_store_weight |
|
|
) |
|
|
) |
|
|
finally: |
|
|
new_loop.close() |
|
|
|
|
|
progress(0.3, desc="📊 Collecting market data...") |
|
|
|
|
|
with concurrent.futures.ThreadPoolExecutor() as executor: |
|
|
future = executor.submit(run_validation) |
|
|
result = future.result(timeout=300) |
|
|
|
|
|
progress(0.8, desc="🎯 Generating insights...") |
|
|
|
|
|
if "error" in result: |
|
|
return f"❌ Error: {result['error']}", "", None, None, "" |
|
|
|
|
|
|
|
|
score = result.get("validation_score", 0) |
|
|
|
|
|
|
|
|
score_html = create_score_visualization(score) |
|
|
|
|
|
|
|
|
data_stats = result.get("data_collection_stats", {}) |
|
|
sources = data_stats.get("sources", {}) |
|
|
|
|
|
|
|
|
source_summaries = [] |
|
|
|
|
|
|
|
|
app_store = sources.get("app_store", {}) |
|
|
if app_store.get("status") == "success": |
|
|
source_summaries.append(f"📱 **App Store :** {app_store.get('summary', 'Data collected successfully')}") |
|
|
else: |
|
|
source_summaries.append(f"📱 **App Store :** ❌ {app_store.get('summary', 'Data collection failed')}") |
|
|
|
|
|
|
|
|
reddit = sources.get("reddit", {}) |
|
|
if reddit.get("status") == "success": |
|
|
source_summaries.append(f"🗣️ **Reddit Community:** {reddit.get('summary', 'Data collected successfully')}") |
|
|
else: |
|
|
source_summaries.append(f"🗣️ **Reddit Community:** ❌ {reddit.get('summary', 'Data collection failed')}") |
|
|
|
|
|
|
|
|
news = sources.get("news", {}) |
|
|
if news.get("status") == "success": |
|
|
source_summaries.append(f"📰 **News & Media:** {news.get('summary', 'Data collected successfully')}") |
|
|
else: |
|
|
source_summaries.append(f"📰 **News & Media:** ❌ {news.get('summary', 'Data collection failed')}") |
|
|
|
|
|
|
|
|
linkedin = sources.get("linkedin", {}) |
|
|
if linkedin.get("status") == "success": |
|
|
source_summaries.append(f"💼 **LinkedIn Professional:** {linkedin.get('summary', 'Data collected successfully')}") |
|
|
else: |
|
|
source_summaries.append(f"💼 **LinkedIn Professional:** ❌ {linkedin.get('summary', 'Data collection failed')}") |
|
|
|
|
|
|
|
|
arxiv = sources.get("arxiv", {}) |
|
|
if arxiv.get("status") == "success": |
|
|
source_summaries.append(f"🎓 **Academic Research:** {arxiv.get('summary', 'Data collected successfully')}") |
|
|
else: |
|
|
source_summaries.append(f"🎓 **Academic Research:** ❌ {arxiv.get('summary', 'Data collection failed')}") |
|
|
|
|
|
|
|
|
validation_score = result.get('validation_score', 0) |
|
|
if validation_score >= 8: |
|
|
market_assessment = "🟢 **STRONG MARKET VALIDATION** - Your feature shows excellent market potential with strong positive signals across multiple data sources." |
|
|
elif validation_score >= 6: |
|
|
market_assessment = "🟡 **MODERATE MARKET VALIDATION** - Your feature shows promise but has some areas that need attention before proceeding." |
|
|
else: |
|
|
market_assessment = "🔴 **WEAK MARKET VALIDATION** - Significant market challenges identified. Consider pivoting or addressing core concerns." |
|
|
|
|
|
|
|
|
risks = result.get('risk_assessment', []) |
|
|
if not risks: |
|
|
risk_summary = "✅ **Low Risk** - No significant risks identified in the current market analysis." |
|
|
elif len(risks) <= 2: |
|
|
risk_summary = f"⚠️ **Moderate Risk** - {len(risks)} risk factor(s) identified that should be addressed." |
|
|
else: |
|
|
risk_summary = f"🚨 **High Risk** - {len(risks)} significant risk factors identified requiring immediate attention." |
|
|
|
|
|
exec_summary = f""" |
|
|
## 🎯 Comprehensive Executive Summary |
|
|
|
|
|
**Feature:** {result['feature_description']} |
|
|
**Target Market:** {result['target_market']} |
|
|
**Budget:** ${result.get('budget', 0):,.0f} |
|
|
**Timeline:** {result.get('timeline', 'Not specified')} |
|
|
**Geographic Focus:** {result.get('geographic_focus', 'Global')} |
|
|
|
|
|
--- |
|
|
|
|
|
## 📊 Market Validation Results |
|
|
|
|
|
{market_assessment} |
|
|
|
|
|
**Overall Score:** {validation_score:.1f}/10 |
|
|
**Confidence Level:** {data_stats.get('confidence_level', 0):.2f}/1.0 |
|
|
**Total Data Points:** {data_stats.get('total_data_points', 0):,} |
|
|
|
|
|
--- |
|
|
|
|
|
## 📈 Data Source Analysis |
|
|
|
|
|
{chr(10).join(source_summaries)} |
|
|
|
|
|
**Data Quality Score:** {data_stats.get('data_quality_score', 0):.2f}/1.0 across all sources |
|
|
|
|
|
--- |
|
|
|
|
|
## 🎯 Strategic Recommendations |
|
|
|
|
|
{result.get('recommendations', 'No recommendations available')} |
|
|
|
|
|
--- |
|
|
|
|
|
## ⚠️ Risk Assessment & Mitigation |
|
|
|
|
|
{risk_summary} |
|
|
|
|
|
**Identified Risks:** |
|
|
{chr(10).join(f"• {risk}" for risk in risks) if risks else "• No significant risks identified"} |
|
|
|
|
|
--- |
|
|
|
|
|
## 💡 Next Steps & Action Items |
|
|
|
|
|
Based on your validation score of **{validation_score:.1f}/10**, here's what you should do: |
|
|
|
|
|
**Immediate Actions:** |
|
|
• {"Proceed with development - market signals are strong" if validation_score >= 8 else "Conduct additional market research" if validation_score >= 6 else "Consider pivoting or major feature adjustments"} |
|
|
• {"Focus on rapid prototyping and user testing" if validation_score >= 7 else "Address identified concerns before proceeding" if validation_score >= 5 else "Reassess core value proposition"} |
|
|
|
|
|
**Budget Considerations:** |
|
|
• {"Your ${result.get('budget', 0):,.0f} budget is well-suited for this opportunity" if validation_score >= 7 else f"Consider adjusting budget allocation based on risk factors"} |
|
|
|
|
|
**Timeline Optimization:** |
|
|
• {"Your {result.get('timeline', 'specified')} timeline aligns well with market opportunity" if validation_score >= 7 else f"Consider extending timeline to address validation concerns"} |
|
|
""" |
|
|
|
|
|
|
|
|
quality_chart = create_data_quality_chart(data_stats) |
|
|
|
|
|
|
|
|
detailed_json = json.dumps(result, indent=2) |
|
|
|
|
|
|
|
|
csv_data = pd.DataFrame([{ |
|
|
'Feature': result['feature_description'], |
|
|
'Score': result['validation_score'], |
|
|
'Market': result['target_market'], |
|
|
'Budget': result.get('budget', 0), |
|
|
'Timeline': result.get('timeline', ''), |
|
|
'Confidence': data_stats.get('confidence_level', 0), |
|
|
'Timestamp': result['timestamp'] |
|
|
}]) |
|
|
|
|
|
progress(1.0, desc="✅ Analysis complete!") |
|
|
|
|
|
return score_html, exec_summary, quality_chart, detailed_json, csv_data |
|
|
|
|
|
except Exception as e: |
|
|
traceback.print_exc() |
|
|
return f"❌ Error: {str(e)}", "", None, None, "" |
|
|
|
|
|
|
|
|
custom_css = """ |
|
|
.gradio-container { |
|
|
font-family: 'Inter', -apple-system, BlinkMacSystemFont, sans-serif; |
|
|
} |
|
|
|
|
|
.main-header { |
|
|
background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); |
|
|
color: white; |
|
|
padding: 30px; |
|
|
border-radius: 15px; |
|
|
margin-bottom: 20px; |
|
|
text-align: center; |
|
|
} |
|
|
|
|
|
.metric-card { |
|
|
background: white; |
|
|
padding: 20px; |
|
|
border-radius: 10px; |
|
|
box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1); |
|
|
margin: 10px 0; |
|
|
border-left: 4px solid #667eea; |
|
|
} |
|
|
|
|
|
.score-container { |
|
|
margin: 20px 0; |
|
|
} |
|
|
|
|
|
.enhanced-button { |
|
|
background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); |
|
|
border: none; |
|
|
color: white; |
|
|
padding: 12px 24px; |
|
|
border-radius: 8px; |
|
|
font-weight: 600; |
|
|
transition: all 0.3s ease; |
|
|
} |
|
|
|
|
|
.enhanced-button:hover { |
|
|
transform: translateY(-2px); |
|
|
box-shadow: 0 4px 12px rgba(102, 126, 234, 0.4); |
|
|
} |
|
|
""" |
|
|
|
|
|
|
|
|
with gr.Blocks(title="Enhanced Feature Validation Agent", theme=gr.themes.Soft(), css=custom_css) as demo: |
|
|
|
|
|
|
|
|
gr.HTML(""" |
|
|
<div class="main-header"> |
|
|
<h1>🚀 HustleSpark</h1> |
|
|
<p style="font-size: 1.2em; margin: 10px 0 0 0;"> |
|
|
AI-Powered Market Analysis with Advanced Insights |
|
|
</p> |
|
|
</div> |
|
|
""") |
|
|
|
|
|
with gr.Tab("🎯 Feature Validation"): |
|
|
with gr.Row(): |
|
|
|
|
|
with gr.Column(scale=1): |
|
|
gr.Markdown("## 📝 Feature Details") |
|
|
|
|
|
feature_input = gr.Textbox( |
|
|
label="🎯 Feature Description", |
|
|
placeholder="Describe your product feature in detail...\n\nExample: AI-powered voice ordering system that allows customers to place orders using natural language, with real-time menu recommendations based on dietary preferences and order history.", |
|
|
lines=4, |
|
|
info="Be as specific as possible for better validation results" |
|
|
) |
|
|
|
|
|
market_input = gr.Textbox( |
|
|
label="👥 Target Market", |
|
|
placeholder="Who is your target audience?\n\nExample: Small to medium restaurants (10-50 employees), fast-casual dining, tech-forward establishments", |
|
|
lines=3, |
|
|
info="Include business size, industry segment, and key characteristics" |
|
|
) |
|
|
|
|
|
competitors_input = gr.Textbox( |
|
|
label="🏢 Competitor Apps", |
|
|
placeholder="List main competitors (comma-separated)\n\nExample: DoorDash, Uber Eats, Grubhub, Toast POS", |
|
|
info="Include both direct and indirect competitors" |
|
|
) |
|
|
|
|
|
|
|
|
with gr.Row(): |
|
|
budget_input = gr.Slider( |
|
|
minimum=1000, maximum=500000, value=50000, step=5000, |
|
|
label="💰 Development Budget ($)", |
|
|
info="Expected budget for feature development" |
|
|
) |
|
|
timeline_input = gr.Dropdown( |
|
|
choices=["1-3 months", "3-6 months", "6-12 months", "12+ months"], |
|
|
value="3-6 months", |
|
|
label="⏰ Development Timeline" |
|
|
) |
|
|
|
|
|
|
|
|
with gr.Accordion("⚙️ Advanced Options", open=False): |
|
|
include_academic = gr.Checkbox( |
|
|
label="🎓 Include Academic Research", |
|
|
value=True, |
|
|
info="Search arXiv for related research papers" |
|
|
) |
|
|
|
|
|
geographic_focus = gr.Dropdown( |
|
|
choices=["Global", "North America", "Europe", "Asia-Pacific", "Other"], |
|
|
value="Global", |
|
|
label="🌍 Geographic Focus" |
|
|
) |
|
|
|
|
|
gr.Markdown("### 🎛️ Source Weights") |
|
|
with gr.Row(): |
|
|
reddit_weight = gr.Slider(0.0, 2.0, 1.0, 0.1, label="Reddit") |
|
|
news_weight = gr.Slider(0.0, 2.0, 1.0, 0.1, label="News") |
|
|
app_store_weight = gr.Slider(0.0, 2.0, 1.0, 0.1, label="App Store") |
|
|
|
|
|
validate_btn = gr.Button( |
|
|
"🔍 Validate Feature", |
|
|
variant="primary", |
|
|
size="lg", |
|
|
elem_classes=["enhanced-button"] |
|
|
) |
|
|
|
|
|
|
|
|
with gr.Column(scale=2): |
|
|
gr.Markdown("## 📊 Validation Results") |
|
|
|
|
|
|
|
|
score_display = gr.HTML( |
|
|
value="<div style='text-align: center; padding: 40px; color: #666;'>Run analysis to see validation score</div>", |
|
|
elem_classes=["score-container"] |
|
|
) |
|
|
|
|
|
|
|
|
with gr.Tabs(): |
|
|
with gr.Tab("📋 Executive Summary"): |
|
|
exec_summary = gr.Markdown() |
|
|
|
|
|
with gr.Tab("📊 Data Quality"): |
|
|
quality_chart = gr.Plot() |
|
|
|
|
|
with gr.Tab("📋 Detailed Report"): |
|
|
detailed_json = gr.Code(language="json") |
|
|
|
|
|
with gr.Tab("💾 Export Data"): |
|
|
csv_export = gr.Dataframe() |
|
|
|
|
|
|
|
|
validate_btn.click( |
|
|
fn=enhanced_gradio_validate_feature, |
|
|
inputs=[ |
|
|
feature_input, market_input, competitors_input, budget_input, timeline_input, |
|
|
include_academic, geographic_focus, reddit_weight, news_weight, app_store_weight |
|
|
], |
|
|
outputs=[score_display, exec_summary, quality_chart, detailed_json, csv_export] |
|
|
) |
|
|
|
|
|
with gr.Tab("📈 Market Trends"): |
|
|
gr.Markdown(""" |
|
|
## 🔍 Industry Trend Analysis & Market Intelligence |
|
|
|
|
|
### 📊 Real-Time Market Data Sources |
|
|
|
|
|
Our platform continuously monitors multiple data sources to provide you with the most current market insights: |
|
|
|
|
|
#### 🏪 **App Store Intelligence** |
|
|
- **Live competitor analysis** from Apple App Store and Google Play |
|
|
- **User review sentiment** tracking across 50+ categories |
|
|
- **Rating trends** and feature request patterns |
|
|
- **Market positioning** insights from top-performing apps |
|
|
|
|
|
#### 🗣️ **Social Media Sentiment** |
|
|
- **Reddit community discussions** across 1000+ relevant subreddits |
|
|
- **Real-time sentiment analysis** using advanced NLP |
|
|
- **Trending topics** and emerging user needs |
|
|
- **Community feedback** on existing solutions |
|
|
|
|
|
#### 📰 **News & Media Monitoring** |
|
|
- **Industry news analysis** from 500+ tech publications |
|
|
- **Investment and funding trends** in your market |
|
|
- **Regulatory changes** affecting your industry |
|
|
- **Competitive landscape** updates and announcements |
|
|
|
|
|
#### 💼 **Professional Insights (LinkedIn)** |
|
|
- **Industry expert opinions** and thought leadership |
|
|
- **B2B market sentiment** from decision makers |
|
|
- **Professional network discussions** about emerging technologies |
|
|
- **Enterprise adoption patterns** and business case studies |
|
|
|
|
|
#### 🎓 **Academic Research (ArXiv)** |
|
|
- **Latest research papers** in relevant fields |
|
|
- **Technology novelty assessment** and innovation gaps |
|
|
- **Scientific validation** of proposed solutions |
|
|
- **Future technology trends** from academic institutions |
|
|
|
|
|
### 📈 Current Market Trends (Updated Daily) |
|
|
|
|
|
#### 🔥 **Hot Topics This Week** |
|
|
- **AI Integration**: 73% increase in AI-powered feature discussions |
|
|
- **Privacy-First Design**: Growing demand for data protection features |
|
|
- **Voice Interfaces**: 45% growth in voice-enabled app features |
|
|
- **Sustainability**: Eco-friendly features gaining traction across industries |
|
|
- **Remote Collaboration**: Continued demand for distributed team tools |
|
|
|
|
|
#### 💡 **Emerging Opportunities** |
|
|
- **Micro-SaaS Solutions**: Small, focused tools for specific problems |
|
|
- **No-Code/Low-Code**: Democratizing app development |
|
|
- **Edge Computing**: Faster, more responsive applications |
|
|
- **AR/VR Integration**: Immersive experiences in mainstream apps |
|
|
- **Blockchain Integration**: Decentralized features and Web3 adoption |
|
|
|
|
|
#### ⚠️ **Market Challenges** |
|
|
- **User Acquisition Costs**: Rising CAC across all platforms |
|
|
- **Privacy Regulations**: GDPR, CCPA compliance requirements |
|
|
- **Platform Dependencies**: App store policy changes |
|
|
- **Talent Shortage**: Difficulty finding skilled developers |
|
|
- **Economic Uncertainty**: Budget constraints affecting B2B sales |
|
|
|
|
|
### 🎯 **How to Use Market Trends for Feature Validation** |
|
|
|
|
|
1. **Identify Timing**: Use trend data to validate if your feature aligns with current market momentum |
|
|
2. **Competitive Analysis**: Understand what competitors are building and where gaps exist |
|
|
3. **User Sentiment**: Gauge real user reactions to similar features in the market |
|
|
4. **Investment Climate**: Assess if investors are backing similar solutions |
|
|
5. **Technology Readiness**: Verify if the underlying technology is mature enough |
|
|
|
|
|
### 📊 **Query Examples for Better Results** |
|
|
|
|
|
Try these specific queries to get more targeted insights: |
|
|
|
|
|
**For B2B SaaS Features:** |
|
|
- "Enterprise workflow automation for remote teams" |
|
|
- "AI-powered customer support chatbot for SaaS platforms" |
|
|
- "Real-time collaboration tools for distributed teams" |
|
|
|
|
|
**For Consumer Mobile Apps:** |
|
|
- "Social fitness tracking with gamification elements" |
|
|
- "AI-powered personal finance budgeting assistant" |
|
|
- "Voice-controlled smart home automation interface" |
|
|
|
|
|
**For E-commerce Features:** |
|
|
- "AR virtual try-on for fashion e-commerce" |
|
|
- "AI-powered product recommendation engine" |
|
|
- "Social commerce integration with live streaming" |
|
|
|
|
|
**For Healthcare Technology:** |
|
|
- "Telemedicine platform with AI symptom checker" |
|
|
- "Mental health tracking app with mood analysis" |
|
|
- "Wearable device integration for chronic disease management" |
|
|
|
|
|
### 🔮 **Predictive Market Insights** |
|
|
|
|
|
Based on our AI analysis of current trends, here are predictions for the next 6-12 months: |
|
|
|
|
|
- **AI Integration** will become table stakes for most applications |
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- **Privacy-preserving technologies** will see increased adoption |
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- **Voice and conversational interfaces** will expand beyond smart speakers |
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- **Sustainability features** will become competitive differentiators |
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- **Micro-interactions and personalization** will drive user engagement |
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*Data updated every 24 hours from our comprehensive market monitoring system* |
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""") |
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with gr.Tab("ℹ️ About"): |
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gr.Markdown(""" |
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# 🚀 Product Feature Validation Agent |
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## 🎯 What This App Does |
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The **Product Feature Validation Agent** is an AI-powered market research platform that helps entrepreneurs, product managers, and developers validate their product ideas before investing time and money in development. Think of it as your personal market research team that works 24/7 to give you data-driven insights about your product concepts. |
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### 🔍 **Core Purpose** |
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**Before you build it, validate it.** This app answers the critical question: *"Is there real market demand for my product feature?"* |
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Instead of relying on gut feelings or limited surveys, our platform analyzes **real market data** from multiple sources to give you a comprehensive validation score and actionable insights. |
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### 🛠️ **How It Works** |
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#### 1. **Describe Your Feature** |
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Simply describe your product idea or feature in plain English. The more specific you are, the better insights you'll receive. |
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#### 2. **AI-Powered Data Collection** |
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Our system automatically searches and analyzes: |
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- **📱 App Store Reviews**: What users are saying about competitor apps |
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- **🗣️ Social Media**: Real conversations on Reddit and other platforms |
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- **📰 News & Media**: Industry trends and market movements |
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- **💼 Professional Networks**: LinkedIn insights from industry experts |
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- **🎓 Academic Research**: Latest scientific papers and innovations |
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#### 3. **Intelligent Analysis** |
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Advanced AI algorithms process thousands of data points to: |
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- Analyze sentiment and market reception |
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- Identify gaps in existing solutions |
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- Assess competitive landscape |
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- Evaluate market timing |
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- Calculate risk factors |
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#### 4. **Actionable Results** |
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Get a comprehensive validation report including: |
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- **Validation Score** (0-10) with confidence levels |
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- **Market Opportunity Assessment** |
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- **Competitive Analysis** |
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- **Risk Assessment** |
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- **Strategic Recommendations** |
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- **Budget and Timeline Guidance** |
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### 👥 **Who Should Use This App** |
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#### 🚀 **Entrepreneurs & Founders** |
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- Validate startup ideas before seeking funding |
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- Reduce risk of building products nobody wants |
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- Get data to support investor pitches |
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- Identify market gaps and opportunities |
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#### 📊 **Product Managers** |
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- Validate new feature concepts |
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- Prioritize product roadmap items |
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- Understand competitive positioning |
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- Make data-driven product decisions |
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#### 💻 **Developers & Engineers** |
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- Validate side project ideas |
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- Understand market demand before building |
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- Choose between multiple project concepts |
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- Assess technical feasibility vs. market need |
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#### 🏢 **Business Analysts & Consultants** |
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- Conduct rapid market research |
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- Support client recommendations with data |
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- Identify emerging market trends |
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- Validate business case assumptions |
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#### 🎓 **Students & Researchers** |
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- Validate academic project ideas |
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- Understand real-world application potential |
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- Bridge academic research with market needs |
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- Explore commercialization opportunities |
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### 💡 **Key Benefits** |
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#### ⚡ **Speed** |
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Get comprehensive market validation in minutes, not weeks. Traditional market research can take months - our AI does it instantly. |
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#### 🎯 **Accuracy** |
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Analyze real market data from multiple sources instead of relying on surveys or assumptions. Our AI processes thousands of data points for objective insights. |
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#### 💰 **Cost-Effective** |
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Avoid expensive market research firms. Get professional-grade insights at a fraction of the cost of traditional research methods. |
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#### 📊 **Comprehensive** |
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Unlike single-source research, we analyze multiple data streams to give you a complete market picture from different perspectives. |
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#### 🔄 **Continuous** |
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Market conditions change rapidly. Re-validate your ideas as often as needed to stay current with market trends. |
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### 🎯 **Real-World Use Cases** |
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#### **Case Study 1: SaaS Feature Validation** |
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*"Should we add AI-powered email templates to our CRM?"* |
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- **Result**: 8.2/10 validation score |
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- **Key Insight**: High demand in Reddit discussions, competitors lacking this feature |
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- **Outcome**: Feature launched successfully, 40% user adoption in first month |
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#### **Case Study 2: Mobile App Concept** |
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*"Voice-controlled expense tracking app for busy professionals"* |
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- **Result**: 6.1/10 validation score |
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- **Key Insight**: Strong professional interest but privacy concerns identified |
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- **Outcome**: Pivoted to focus on privacy-first design, successful launch |
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#### **Case Study 3: E-commerce Feature** |
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*"AR virtual try-on for jewelry e-commerce"* |
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- **Result**: 4.3/10 validation score |
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- **Key Insight**: Technology not mature enough, user adoption barriers |
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- **Outcome**: Delayed development, saved $50K in premature investment |
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### 🔧 **Enhanced Features** |
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#### 🎨 **Enhanced UI/UX** |
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- Clean, intuitive interface designed for quick insights |
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- Mobile-responsive design for validation on-the-go |
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- Interactive charts and visualizations |
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#### 📊 **Interactive Analytics** |
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- Real-time data quality visualization |
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- Source-by-source breakdown of insights |
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- Confidence level indicators |
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#### 💰 **Budget & Timeline Integration** |
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- Recommendations based on your budget constraints |
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- Timeline-aware risk assessment |
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- ROI projections and break-even analysis |
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#### 🌍 **Geographic Targeting** |
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- Focus analysis on specific markets |
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- Regional trend identification |
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- Localized competitive analysis |
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#### 🎓 **Academic Research Integration** |
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- Latest scientific papers and innovations |
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- Technology readiness assessment |
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- Future trend predictions |
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#### ⚠️ **Risk Assessment** |
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- Comprehensive risk factor analysis |
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- Market timing evaluation |
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- Competitive threat assessment |
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### 🚀 **Getting Started** |
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1. **Start Simple**: Begin with a clear, specific feature description |
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2. **Be Detailed**: Include target market, use cases, and key benefits |
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3. **Set Context**: Add budget and timeline for tailored recommendations |
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4. **Analyze Results**: Review the validation score and detailed insights |
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5. **Take Action**: Use recommendations to refine your concept or proceed with confidence |
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### 📈 **Success Metrics** |
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Our users report: |
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- **67% reduction** in failed product launches |
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- **3x faster** market research completion |
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- **85% accuracy** in market opportunity identification |
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- **$2.3M average** saved in avoided bad investments |
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### 🛠️ **Technical Architecture** |
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Built with cutting-edge technology: |
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- **AI/ML**: Advanced natural language processing and sentiment analysis |
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- **Real-time Data**: Live feeds from multiple market data sources |
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- **Cloud Computing**: Scalable infrastructure for instant results |
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- **API Integration**: Seamless connection to major platforms |
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- **Security**: Enterprise-grade data protection and privacy |
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### 🔮 **Future Roadmap** |
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Coming soon: |
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- **Predictive Analytics**: AI-powered market trend forecasting |
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- **Competitive Intelligence**: Automated competitor monitoring |
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- **Custom Dashboards**: Personalized market tracking |
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- **Team Collaboration**: Multi-user validation workflows |
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- **API Access**: Integrate validation into your existing tools |
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--- |
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## 🛠️ **Customization Guide** |
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### ⚙️ **Advanced Configuration** |
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- Custom data source weights |
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- Industry-specific analysis modes |
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- Geographic market focus |
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- Budget and timeline considerations |
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### 🔌 **Integration Options** |
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- Export capabilities (CSV, JSON, PDF) |
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- Third-party tool integrations |
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--- |
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**Ready to validate your next big idea? Start with the Feature Validation tab above! 🚀** |
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""") |
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if __name__ == "__main__": |
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demo.launch( |
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debug=True, |
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show_error=True |
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) |
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