import os
import re
from http import HTTPStatus
from typing import Dict, List, Optional, Tuple
import base64
import mimetypes
import PyPDF2
import docx
import cv2
import numpy as np
from PIL import Image
import pytesseract
import requests
from urllib.parse import urlparse, urljoin
from bs4 import BeautifulSoup
import html2text
import json
import time
import webbrowser
import urllib.parse
import copy
import html
import gradio as gr
from huggingface_hub import InferenceClient
from tavily import TavilyClient
from huggingface_hub import HfApi
import tempfile
from openai import OpenAI
from mistralai import Mistral
# Gradio supported languages for syntax highlighting
GRADIO_SUPPORTED_LANGUAGES = [
"python", "c", "cpp", "markdown", "latex", "json", "html", "css", "javascript", "jinja2", "typescript", "yaml", "dockerfile", "shell", "r", "sql", "sql-msSQL", "sql-mySQL", "sql-mariaDB", "sql-sqlite", "sql-cassandra", "sql-plSQL", "sql-hive", "sql-pgSQL", "sql-gql", "sql-gpSQL", "sql-sparkSQL", "sql-esper", None
]
def get_gradio_language(language):
# Map composite options to a supported syntax highlighting
if language == "streamlit":
return "python"
if language == "gradio":
return "python"
return language if language in GRADIO_SUPPORTED_LANGUAGES else None
# Search/Replace Constants
SEARCH_START = "<<<<<<< SEARCH"
DIVIDER = "======="
REPLACE_END = ">>>>>>> REPLACE"
# Configuration
HTML_SYSTEM_PROMPT = """ONLY USE HTML, CSS AND JAVASCRIPT. If you want to use ICON make sure to import the library first. Try to create the best UI possible by using only HTML, CSS and JAVASCRIPT. MAKE IT RESPONSIVE USING MODERN CSS. Use as much as you can modern CSS for the styling, if you can't do something with modern CSS, then use custom CSS. Also, try to elaborate as much as you can, to create something unique. ALWAYS GIVE THE RESPONSE INTO A SINGLE HTML FILE
For website redesign tasks:
- Use the provided original HTML code as the starting point for redesign
- Preserve all original content, structure, and functionality
- Keep the same semantic HTML structure but enhance the styling
- Reuse all original images and their URLs from the HTML code
- Create a modern, responsive design with improved typography and spacing
- Use modern CSS frameworks and design patterns
- Ensure accessibility and mobile responsiveness
- Maintain the same navigation and user flow
- Enhance the visual design while keeping the original layout structure
If an image is provided, analyze it and use the visual information to better understand the user's requirements.
Always respond with code that can be executed or rendered directly.
Always output only the HTML code inside a ```html ... ``` code block, and do not include any explanations or extra text. Do NOT add the language name at the top of the code output."""
# Stricter prompt for GLM-4.5V to ensure a complete, runnable HTML document with no escaped characters
GLM45V_HTML_SYSTEM_PROMPT = """You are an expert front-end developer.
Output a COMPLETE, STANDALONE HTML document that renders directly in a browser.
Hard constraints:
- DO NOT use React, ReactDOM, JSX, Babel, Vue, Angular, Svelte, or any SPA framework.
- Use ONLY plain HTML, CSS, and vanilla JavaScript.
- Allowed external resources: Tailwind CSS CDN, Font Awesome CDN, Google Fonts.
- Do NOT escape characters (no \\n, \\t, or escaped quotes). Output raw HTML/JS/CSS.
Structural requirements:
- Include , , , and with proper nesting
- Include required tags for any CSS you reference (e.g., Tailwind, Font Awesome, Google Fonts)
- Keep everything in ONE file; inline CSS/JS as needed
Return ONLY the code inside a single ```html ... ``` code block. No additional text before or after.
"""
TRANSFORMERS_JS_SYSTEM_PROMPT = """You are an expert web developer creating a transformers.js application. You will generate THREE separate files: index.html, index.js, and style.css.
IMPORTANT: You MUST output ALL THREE files in the following format:
```html
```
```javascript
// index.js content here
```
```css
/* style.css content here */
```
Requirements:
1. Create a modern, responsive web application using transformers.js
2. Use the transformers.js library for AI/ML functionality
3. Create a clean, professional UI with good user experience
4. Make the application fully responsive for mobile devices
5. Use modern CSS practices and JavaScript ES6+ features
6. Include proper error handling and loading states
7. Follow accessibility best practices
The index.html should contain the basic HTML structure and link to the CSS and JS files.
The index.js should contain all the JavaScript logic including transformers.js integration.
The style.css should contain all the styling for the application.
Always output only the three code blocks as shown above, and do not include any explanations or extra text."""
SVELTE_SYSTEM_PROMPT = """You are an expert Svelte developer creating a modern Svelte application. You will generate ONLY the custom files that need user-specific content for the user's requested application.
IMPORTANT: You MUST output files in the following format. Generate ONLY the files needed for the user's specific request:
```svelte
```
```css
/* src/app.css content here */
```
If you need additional components for the user's specific app, add them like:
```svelte
```
Requirements:
1. Create a modern, responsive Svelte application based on the user's specific request
2. Use TypeScript for better type safety
3. Create a clean, professional UI with good user experience
4. Make the application fully responsive for mobile devices
5. Use modern CSS practices and Svelte best practices
6. Include proper error handling and loading states
7. Follow accessibility best practices
8. Use Svelte's reactive features effectively
9. Include proper component structure and organization
10. Generate ONLY components that are actually needed for the user's requested application
Files you should generate:
- src/App.svelte: Main application component (ALWAYS required)
- src/app.css: Global styles (ALWAYS required)
- src/lib/[ComponentName].svelte: Additional components (ONLY if needed for the user's specific app)
The other files (index.html, package.json, vite.config.ts, tsconfig files, svelte.config.js, src/main.ts, src/vite-env.d.ts) are provided by the Svelte template and don't need to be generated.
Always output only the two code blocks as shown above, and do not include any explanations or extra text."""
SVELTE_SYSTEM_PROMPT_WITH_SEARCH = """You are an expert Svelte developer creating a modern Svelte application. You have access to real-time web search. When needed, use web search to find the latest information, best practices, or specific Svelte technologies.
You will generate ONLY the custom files that need user-specific content.
IMPORTANT: You MUST output ONLY the custom files in the following format:
```svelte
```
```css
/* src/app.css content here -->
```
Requirements:
1. Create a modern, responsive Svelte application
2. Use TypeScript for better type safety
3. Create a clean, professional UI with good user experience
4. Make the application fully responsive for mobile devices
5. Use modern CSS practices and Svelte best practices
6. Include proper error handling and loading states
7. Follow accessibility best practices
8. Use Svelte's reactive features effectively
9. Include proper component structure and organization
10. Use web search to find the latest Svelte patterns, libraries, and best practices
The files you generate are:
- src/App.svelte: Main application component (your custom app logic)
- src/app.css: Global styles (your custom styling)
The other files (index.html, package.json, vite.config.ts, tsconfig files, svelte.config.js, src/main.ts, src/vite-env.d.ts) are provided by the Svelte template and don't need to be generated.
Always output only the two code blocks as shown above, and do not include any explanations or extra text."""
TRANSFORMERS_JS_SYSTEM_PROMPT_WITH_SEARCH = """You are an expert web developer creating a transformers.js application. You have access to real-time web search. When needed, use web search to find the latest information, best practices, or specific technologies for transformers.js.
You will generate THREE separate files: index.html, index.js, and style.css.
IMPORTANT: You MUST output ALL THREE files in the following format:
```html
```
```javascript
// index.js content here
```
```css
/* style.css content here */
```
Requirements:
1. Create a modern, responsive web application using transformers.js
2. Use the transformers.js library for AI/ML functionality
3. Use web search to find current best practices and latest transformers.js features
4. Create a clean, professional UI with good user experience
5. Make the application fully responsive for mobile devices
6. Use modern CSS practices and JavaScript ES6+ features
7. Include proper error handling and loading states
8. Follow accessibility best practices
The index.html should contain the basic HTML structure and link to the CSS and JS files.
The index.js should contain all the JavaScript logic including transformers.js integration.
The style.css should contain all the styling for the application.
Always output only the three code blocks as shown above, and do not include any explanations or extra text."""
GENERIC_SYSTEM_PROMPT = """You are an expert {language} developer. Write clean, idiomatic, and runnable {language} code for the user's request. If possible, include comments and best practices. Output ONLY the code inside a ``` code block, and do not include any explanations or extra text. If the user provides a file or other context, use it as a reference. If the code is for a script or app, make it as self-contained as possible. Do NOT add the language name at the top of the code output."""
# System prompt with search capability
HTML_SYSTEM_PROMPT_WITH_SEARCH = """You are an expert front-end developer. You have access to real-time web search.
Output a COMPLETE, STANDALONE HTML document that renders directly in a browser. Requirements:
- Include , , , and with proper nesting
- Include all required and
{REPLACE_END}
```
Example Fixing Dependencies (requirements.txt):
```
Adding missing dependency to fix ImportError...
=== requirements.txt ===
{SEARCH_START}
gradio
streamlit
{DIVIDER}
gradio
streamlit
mistral-common
{REPLACE_END}
```
Example Deleting Code:
```
Removing the paragraph...
{SEARCH_START}
This paragraph will be deleted.
{DIVIDER}
{REPLACE_END}
```"""
# Follow-up system prompt for modifying existing transformers.js applications
TransformersJSFollowUpSystemPrompt = f"""You are an expert web developer modifying an existing transformers.js application.
The user wants to apply changes based on their request.
You MUST output ONLY the changes required using the following SEARCH/REPLACE block format. Do NOT output the entire file.
Explain the changes briefly *before* the blocks if necessary, but the code changes THEMSELVES MUST be within the blocks.
IMPORTANT: When the user reports an ERROR MESSAGE, analyze it carefully to determine which file needs fixing:
- JavaScript errors/module loading issues → Fix index.js
- HTML rendering/DOM issues → Fix index.html
- Styling/visual issues → Fix style.css
- CDN/library loading errors → Fix script tags in index.html
The transformers.js application consists of three files: index.html, index.js, and style.css.
When making changes, specify which file you're modifying by starting your search/replace blocks with the file name.
Format Rules:
1. Start with {SEARCH_START}
2. Provide the exact lines from the current code that need to be replaced.
3. Use {DIVIDER} to separate the search block from the replacement.
4. Provide the new lines that should replace the original lines.
5. End with {REPLACE_END}
6. You can use multiple SEARCH/REPLACE blocks if changes are needed in different parts of the file.
7. To insert code, use an empty SEARCH block (only {SEARCH_START} and {DIVIDER} on their lines) if inserting at the very beginning, otherwise provide the line *before* the insertion point in the SEARCH block and include that line plus the new lines in the REPLACE block.
8. To delete code, provide the lines to delete in the SEARCH block and leave the REPLACE block empty (only {DIVIDER} and {REPLACE_END} on their lines).
9. IMPORTANT: The SEARCH block must *exactly* match the current code, including indentation and whitespace.
Example Modifying HTML:
```
Changing the title in index.html...
=== index.html ===
{SEARCH_START}
Old Title
{DIVIDER}
New Title
{REPLACE_END}
```
Example Modifying JavaScript:
```
Adding a new function to index.js...
=== index.js ===
{SEARCH_START}
// Existing code
{DIVIDER}
// Existing code
function newFunction() {{
console.log("New function added");
}}
{REPLACE_END}
```
Example Modifying CSS:
```
Changing background color in style.css...
=== style.css ===
{SEARCH_START}
body {{
background-color: white;
}}
{DIVIDER}
body {{
background-color: #f0f0f0;
}}
{REPLACE_END}
```
Example Fixing Library Loading Error:
```
Fixing transformers.js CDN loading error...
=== index.html ===
{SEARCH_START}
{DIVIDER}
{REPLACE_END}
```"""
# Available models
AVAILABLE_MODELS = [
{
"name": "Moonshot Kimi-K2",
"id": "moonshotai/Kimi-K2-Instruct",
"description": "Moonshot AI Kimi-K2-Instruct model for code generation and general tasks"
},
{
"name": "DeepSeek V3",
"id": "deepseek-ai/DeepSeek-V3-0324",
"description": "DeepSeek V3 model for code generation"
},
{
"name": "DeepSeek R1",
"id": "deepseek-ai/DeepSeek-R1-0528",
"description": "DeepSeek R1 model for code generation"
},
{
"name": "ERNIE-4.5-VL",
"id": "baidu/ERNIE-4.5-VL-424B-A47B-Base-PT",
"description": "ERNIE-4.5-VL model for multimodal code generation with image support"
},
{
"name": "MiniMax M1",
"id": "MiniMaxAI/MiniMax-M1-80k",
"description": "MiniMax M1 model for code generation and general tasks"
},
{
"name": "Qwen3-235B-A22B",
"id": "Qwen/Qwen3-235B-A22B",
"description": "Qwen3-235B-A22B model for code generation and general tasks"
},
{
"name": "SmolLM3-3B",
"id": "HuggingFaceTB/SmolLM3-3B",
"description": "SmolLM3-3B model for code generation and general tasks"
},
{
"name": "GLM-4.5",
"id": "zai-org/GLM-4.5",
"description": "GLM-4.5 model with thinking capabilities for advanced code generation"
},
{
"name": "GLM-4.5V",
"id": "zai-org/GLM-4.5V",
"description": "GLM-4.5V multimodal model with image understanding for code generation"
},
{
"name": "GLM-4.1V-9B-Thinking",
"id": "THUDM/GLM-4.1V-9B-Thinking",
"description": "GLM-4.1V-9B-Thinking model for multimodal code generation with image support"
},
{
"name": "Qwen3-235B-A22B-Instruct-2507",
"id": "Qwen/Qwen3-235B-A22B-Instruct-2507",
"description": "Qwen3-235B-A22B-Instruct-2507 model for code generation and general tasks"
},
{
"name": "Qwen3-Coder-480B-A35B-Instruct",
"id": "Qwen/Qwen3-Coder-480B-A35B-Instruct",
"description": "Qwen3-Coder-480B-A35B-Instruct model for advanced code generation and programming tasks"
},
{
"name": "Qwen3-32B",
"id": "Qwen/Qwen3-32B",
"description": "Qwen3-32B model for code generation and general tasks"
},
{
"name": "Qwen3-4B-Instruct-2507",
"id": "Qwen/Qwen3-4B-Instruct-2507",
"description": "Qwen3-4B-Instruct-2507 model for code generation and general tasks"
},
{
"name": "Qwen3-4B-Thinking-2507",
"id": "Qwen/Qwen3-4B-Thinking-2507",
"description": "Qwen3-4B-Thinking-2507 model with advanced reasoning capabilities for code generation and general tasks"
},
{
"name": "Qwen3-235B-A22B-Thinking",
"id": "Qwen/Qwen3-235B-A22B-Thinking-2507",
"description": "Qwen3-235B-A22B-Thinking model with advanced reasoning capabilities"
},
{
"name": "Qwen3-30B-A3B-Instruct-2507",
"id": "qwen3-30b-a3b-instruct-2507",
"description": "Qwen3-30B-A3B-Instruct model via Alibaba Cloud DashScope API"
},
{
"name": "Qwen3-30B-A3B-Thinking-2507",
"id": "qwen3-30b-a3b-thinking-2507",
"description": "Qwen3-30B-A3B-Thinking model with advanced reasoning via Alibaba Cloud DashScope API"
},
{
"name": "Qwen3-Coder-30B-A3B-Instruct",
"id": "qwen3-coder-30b-a3b-instruct",
"description": "Qwen3-Coder-30B-A3B-Instruct model for advanced code generation via Alibaba Cloud DashScope API"
},
{
"name": "StepFun Step-3",
"id": "step-3",
"description": "StepFun Step-3 model - AI chat assistant by 阶跃星辰 with multilingual capabilities"
},
{
"name": "Codestral 2508",
"id": "codestral-2508",
"description": "Mistral Codestral model - specialized for code generation and programming tasks"
},
{
"name": "Gemini 2.5 Flash",
"id": "gemini-2.5-flash",
"description": "Google Gemini 2.5 Flash via OpenAI-compatible API"
},
{
"name": "Gemini 2.5 Pro",
"id": "gemini-2.5-pro",
"description": "Google Gemini 2.5 Pro via OpenAI-compatible API"
},
{
"name": "GPT-OSS-120B",
"id": "openai/gpt-oss-120b",
"description": "OpenAI GPT-OSS-120B model for advanced code generation and general tasks"
},
{
"name": "GPT-OSS-20B",
"id": "openai/gpt-oss-20b",
"description": "OpenAI GPT-OSS-20B model for code generation and general tasks"
},
{
"name": "GPT-5",
"id": "gpt-5",
"description": "OpenAI GPT-5 model for advanced code generation and general tasks"
},
{
"name": "Grok-4",
"id": "grok-4",
"description": "Grok-4 model via Poe (OpenAI-compatible) for advanced tasks"
}
]
# Default model selection
DEFAULT_MODEL_NAME = "Grok-4"
DEFAULT_MODEL = None
for _m in AVAILABLE_MODELS:
if _m.get("name") == DEFAULT_MODEL_NAME:
DEFAULT_MODEL = _m
break
if DEFAULT_MODEL is None and AVAILABLE_MODELS:
DEFAULT_MODEL = AVAILABLE_MODELS[0]
DEMO_LIST = [
{
"title": "Todo App",
"description": "Create a simple todo application with add, delete, and mark as complete functionality"
},
{
"title": "Calculator",
"description": "Build a basic calculator with addition, subtraction, multiplication, and division"
},
{
"title": "Chat Interface",
"description": "Build a chat interface with message history and user input"
},
{
"title": "E-commerce Product Card",
"description": "Create a product card component for an e-commerce website"
},
{
"title": "Login Form",
"description": "Build a responsive login form with validation"
},
{
"title": "Dashboard Layout",
"description": "Create a dashboard layout with sidebar navigation and main content area"
},
{
"title": "Data Table",
"description": "Build a data table with sorting and filtering capabilities"
},
{
"title": "Image Gallery",
"description": "Create an image gallery with lightbox functionality and responsive grid layout"
},
{
"title": "UI from Image",
"description": "Upload an image of a UI design and I'll generate the HTML/CSS code for it"
},
{
"title": "Extract Text from Image",
"description": "Upload an image containing text and I'll extract and process the text content"
},
{
"title": "Website Redesign",
"description": "Enter a website URL to extract its content and redesign it with a modern, responsive layout"
},
{
"title": "Modify HTML",
"description": "After generating HTML, ask me to modify it with specific changes using search/replace format"
},
{
"title": "Search/Replace Example",
"description": "Generate HTML first, then ask: 'Change the title to My New Title' or 'Add a blue background to the body'"
},
{
"title": "Transformers.js App",
"description": "Create a transformers.js application with AI/ML functionality using the transformers.js library"
},
{
"title": "Svelte App",
"description": "Create a modern Svelte application with TypeScript, Vite, and responsive design"
}
]
# HF Inference Client
HF_TOKEN = os.getenv('HF_TOKEN')
if not HF_TOKEN:
raise RuntimeError("HF_TOKEN environment variable is not set. Please set it to your Hugging Face API token.")
def get_inference_client(model_id, provider="auto"):
"""Return an InferenceClient with provider based on model_id and user selection."""
if model_id == "qwen3-30b-a3b-instruct-2507":
# Use DashScope OpenAI client
return OpenAI(
api_key=os.getenv("DASHSCOPE_API_KEY"),
base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
)
elif model_id == "qwen3-30b-a3b-thinking-2507":
# Use DashScope OpenAI client for Thinking model
return OpenAI(
api_key=os.getenv("DASHSCOPE_API_KEY"),
base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
)
elif model_id == "qwen3-coder-30b-a3b-instruct":
# Use DashScope OpenAI client for Coder model
return OpenAI(
api_key=os.getenv("DASHSCOPE_API_KEY"),
base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
)
elif model_id == "gpt-5":
# Use Poe (OpenAI-compatible) client for GPT-5 model
return OpenAI(
api_key=os.getenv("POE_API_KEY"),
base_url="https://api.poe.com/v1"
)
elif model_id == "grok-4":
# Use Poe (OpenAI-compatible) client for Grok-4 model
return OpenAI(
api_key=os.getenv("POE_API_KEY"),
base_url="https://api.poe.com/v1"
)
elif model_id == "step-3":
# Use StepFun API client for Step-3 model
return OpenAI(
api_key=os.getenv("STEP_API_KEY"),
base_url="https://api.stepfun.com/v1"
)
elif model_id == "codestral-2508":
# Use Mistral client for Codestral model
return Mistral(api_key=os.getenv("MISTRAL_API_KEY"))
elif model_id == "gemini-2.5-flash":
# Use Google Gemini (OpenAI-compatible) client
return OpenAI(
api_key=os.getenv("GEMINI_API_KEY"),
base_url="https://generativelanguage.googleapis.com/v1beta/openai/",
)
elif model_id == "gemini-2.5-pro":
# Use Google Gemini Pro (OpenAI-compatible) client
return OpenAI(
api_key=os.getenv("GEMINI_API_KEY"),
base_url="https://generativelanguage.googleapis.com/v1beta/openai/",
)
elif model_id == "openai/gpt-oss-120b":
provider = "cerebras"
elif model_id == "openai/gpt-oss-20b":
provider = "groq"
elif model_id == "moonshotai/Kimi-K2-Instruct":
provider = "groq"
elif model_id == "Qwen/Qwen3-235B-A22B":
provider = "cerebras"
elif model_id == "Qwen/Qwen3-235B-A22B-Instruct-2507":
provider = "cerebras"
elif model_id == "Qwen/Qwen3-32B":
provider = "cerebras"
elif model_id == "Qwen/Qwen3-235B-A22B-Thinking-2507":
provider = "cerebras"
elif model_id == "Qwen/Qwen3-Coder-480B-A35B-Instruct":
provider = "cerebras"
return InferenceClient(
provider=provider,
api_key=HF_TOKEN,
bill_to="huggingface"
)
# Type definitions
History = List[Tuple[str, str]]
Messages = List[Dict[str, str]]
# Tavily Search Client
TAVILY_API_KEY = os.getenv('TAVILY_API_KEY')
tavily_client = None
if TAVILY_API_KEY:
try:
tavily_client = TavilyClient(api_key=TAVILY_API_KEY)
except Exception as e:
print(f"Failed to initialize Tavily client: {e}")
tavily_client = None
def history_to_messages(history: History, system: str) -> Messages:
messages = [{'role': 'system', 'content': system}]
for h in history:
# Handle multimodal content in history
user_content = h[0]
if isinstance(user_content, list):
# Extract text from multimodal content
text_content = ""
for item in user_content:
if isinstance(item, dict) and item.get("type") == "text":
text_content += item.get("text", "")
user_content = text_content if text_content else str(user_content)
messages.append({'role': 'user', 'content': user_content})
messages.append({'role': 'assistant', 'content': h[1]})
return messages
def messages_to_history(messages: Messages) -> Tuple[str, History]:
assert messages[0]['role'] == 'system'
history = []
for q, r in zip(messages[1::2], messages[2::2]):
# Extract text content from multimodal messages for history
user_content = q['content']
if isinstance(user_content, list):
text_content = ""
for item in user_content:
if isinstance(item, dict) and item.get("type") == "text":
text_content += item.get("text", "")
user_content = text_content if text_content else str(user_content)
history.append([user_content, r['content']])
return history
def history_to_chatbot_messages(history: History) -> List[Dict[str, str]]:
"""Convert history tuples to chatbot message format"""
messages = []
for user_msg, assistant_msg in history:
# Handle multimodal content
if isinstance(user_msg, list):
text_content = ""
for item in user_msg:
if isinstance(item, dict) and item.get("type") == "text":
text_content += item.get("text", "")
user_msg = text_content if text_content else str(user_msg)
messages.append({"role": "user", "content": user_msg})
messages.append({"role": "assistant", "content": assistant_msg})
return messages
def remove_code_block(text):
# Try to match code blocks with language markers
patterns = [
r'```(?:html|HTML)\n([\s\S]+?)\n```', # Match ```html or ```HTML
r'```\n([\s\S]+?)\n```', # Match code blocks without language markers
r'```([\s\S]+?)```' # Match code blocks without line breaks
]
for pattern in patterns:
match = re.search(pattern, text, re.DOTALL)
if match:
extracted = match.group(1).strip()
# Remove a leading language marker line (e.g., 'python') if present
if extracted.split('\n', 1)[0].strip().lower() in ['python', 'html', 'css', 'javascript', 'json', 'c', 'cpp', 'markdown', 'latex', 'jinja2', 'typescript', 'yaml', 'dockerfile', 'shell', 'r', 'sql', 'sql-mssql', 'sql-mysql', 'sql-mariadb', 'sql-sqlite', 'sql-cassandra', 'sql-plSQL', 'sql-hive', 'sql-pgsql', 'sql-gql', 'sql-gpsql', 'sql-sparksql', 'sql-esper']:
return extracted.split('\n', 1)[1] if '\n' in extracted else ''
# If HTML markup starts later in the block (e.g., Poe injected preface), trim to first HTML root
html_root_idx = None
for tag in [' 0:
return extracted[html_root_idx:].strip()
return extracted
# If no code block is found, check if the entire text is HTML
stripped = text.strip()
if stripped.startswith('') or stripped.startswith(' 0:
return stripped[idx:].strip()
return stripped
# Special handling for python: remove python marker
if text.strip().startswith('```python'):
return text.strip()[9:-3].strip()
# Remove a leading language marker line if present (fallback)
lines = text.strip().split('\n', 1)
if lines[0].strip().lower() in ['python', 'html', 'css', 'javascript', 'json', 'c', 'cpp', 'markdown', 'latex', 'jinja2', 'typescript', 'yaml', 'dockerfile', 'shell', 'r', 'sql', 'sql-mssql', 'sql-mysql', 'sql-mariadb', 'sql-sqlite', 'sql-cassandra', 'sql-plSQL', 'sql-hive', 'sql-pgsql', 'sql-gql', 'sql-gpsql', 'sql-sparksql', 'sql-esper']:
return lines[1] if len(lines) > 1 else ''
return text.strip()
## React CDN compatibility fixer removed per user preference
def strip_placeholder_thinking(text: str) -> str:
"""Remove placeholder 'Thinking...' status lines from streamed text."""
if not text:
return text
# Matches lines like: "Thinking..." or "Thinking... (12s elapsed)"
return re.sub(r"(?mi)^[\t ]*Thinking\.\.\.(?:\s*\(\d+s elapsed\))?[\t ]*$\n?", "", text)
def is_placeholder_thinking_only(text: str) -> bool:
"""Return True if text contains only 'Thinking...' placeholder lines (with optional elapsed)."""
if not text:
return False
stripped = text.strip()
if not stripped:
return False
return re.fullmatch(r"(?s)(?:\s*Thinking\.\.\.(?:\s*\(\d+s elapsed\))?\s*)+", stripped) is not None
def extract_last_thinking_line(text: str) -> str:
"""Extract the last 'Thinking...' line to display as status."""
matches = list(re.finditer(r"Thinking\.\.\.(?:\s*\(\d+s elapsed\))?", text))
return matches[-1].group(0) if matches else "Thinking..."
def parse_transformers_js_output(text):
"""Parse transformers.js output and extract the three files (index.html, index.js, style.css)"""
files = {
'index.html': '',
'index.js': '',
'style.css': ''
}
# Multiple patterns to match the three code blocks with different variations
html_patterns = [
r'```html\s*\n([\s\S]+?)\n```',
r'```htm\s*\n([\s\S]+?)\n```',
r'```\s*(?:index\.html|html)\s*\n([\s\S]+?)\n```'
]
js_patterns = [
r'```javascript\s*\n([\s\S]+?)\n```',
r'```js\s*\n([\s\S]+?)\n```',
r'```\s*(?:index\.js|javascript)\s*\n([\s\S]+?)\n```'
]
css_patterns = [
r'```css\s*\n([\s\S]+?)\n```',
r'```\s*(?:style\.css|css)\s*\n([\s\S]+?)\n```'
]
# Extract HTML content
for pattern in html_patterns:
html_match = re.search(pattern, text, re.IGNORECASE)
if html_match:
files['index.html'] = html_match.group(1).strip()
break
# Extract JavaScript content
for pattern in js_patterns:
js_match = re.search(pattern, text, re.IGNORECASE)
if js_match:
files['index.js'] = js_match.group(1).strip()
break
# Extract CSS content
for pattern in css_patterns:
css_match = re.search(pattern, text, re.IGNORECASE)
if css_match:
files['style.css'] = css_match.group(1).strip()
break
# Fallback: support === index.html === format if any file is missing
if not (files['index.html'] and files['index.js'] and files['style.css']):
# Use regex to extract sections
html_fallback = re.search(r'===\s*index\.html\s*===\s*\n([\s\S]+?)(?=\n===|$)', text, re.IGNORECASE)
js_fallback = re.search(r'===\s*index\.js\s*===\s*\n([\s\S]+?)(?=\n===|$)', text, re.IGNORECASE)
css_fallback = re.search(r'===\s*style\.css\s*===\s*\n([\s\S]+?)(?=\n===|$)', text, re.IGNORECASE)
if html_fallback:
files['index.html'] = html_fallback.group(1).strip()
if js_fallback:
files['index.js'] = js_fallback.group(1).strip()
if css_fallback:
files['style.css'] = css_fallback.group(1).strip()
# Additional fallback: extract from numbered sections or file headers
if not (files['index.html'] and files['index.js'] and files['style.css']):
# Try patterns like "1. index.html:" or "**index.html**"
patterns = [
(r'(?:^\d+\.\s*|^##\s*|^\*\*\s*)index\.html(?:\s*:|\*\*:?)\s*\n([\s\S]+?)(?=\n(?:\d+\.|##|\*\*|===)|$)', 'index.html'),
(r'(?:^\d+\.\s*|^##\s*|^\*\*\s*)index\.js(?:\s*:|\*\*:?)\s*\n([\s\S]+?)(?=\n(?:\d+\.|##|\*\*|===)|$)', 'index.js'),
(r'(?:^\d+\.\s*|^##\s*|^\*\*\s*)style\.css(?:\s*:|\*\*:?)\s*\n([\s\S]+?)(?=\n(?:\d+\.|##|\*\*|===)|$)', 'style.css')
]
for pattern, file_key in patterns:
if not files[file_key]:
match = re.search(pattern, text, re.IGNORECASE | re.MULTILINE)
if match:
# Clean up the content by removing any code block markers
content = match.group(1).strip()
content = re.sub(r'^```\w*\s*\n', '', content)
content = re.sub(r'\n```\s*$', '', content)
files[file_key] = content.strip()
return files
def format_transformers_js_output(files):
"""Format the three files into a single display string"""
output = []
output.append("=== index.html ===")
output.append(files['index.html'])
output.append("\n=== index.js ===")
output.append(files['index.js'])
output.append("\n=== style.css ===")
output.append(files['style.css'])
return '\n'.join(output)
def build_transformers_inline_html(files: dict) -> str:
"""Merge transformers.js three-file output into a single self-contained HTML document.
- Inlines style.css into a " if css else ""
if style_tag:
if '' in doc.lower():
# Preserve original casing by finding closing head case-insensitively
match = _re.search(r"", doc, flags=_re.IGNORECASE)
if match:
idx = match.start()
doc = doc[:idx] + style_tag + doc[idx:]
else:
# No head; insert at top of body
match = _re.search(r"]*>", doc, flags=_re.IGNORECASE)
if match:
idx = match.end()
doc = doc[:idx] + "\n" + style_tag + doc[idx:]
else:
# Append at beginning
doc = style_tag + doc
# Inline JS: insert before
script_tag = f"" if js else ""
# Cleanup script to clear Cache Storage and IndexedDB on unload to free model weights
cleanup_tag = (
""
)
if script_tag:
match = _re.search(r"", doc, flags=_re.IGNORECASE)
if match:
idx = match.start()
doc = doc[:idx] + script_tag + cleanup_tag + doc[idx:]
else:
# Append at end
doc = doc + script_tag + cleanup_tag
return doc
def send_transformers_to_sandbox(files: dict) -> str:
"""Build a self-contained HTML document from transformers.js files and return an iframe preview."""
merged_html = build_transformers_inline_html(files)
return send_to_sandbox(merged_html)
def parse_svelte_output(text):
"""Parse Svelte output to extract individual files"""
files = {
'src/App.svelte': '',
'src/app.css': ''
}
import re
# First try to extract using code block patterns
svelte_pattern = r'```svelte\s*\n([\s\S]+?)\n```'
css_pattern = r'```css\s*\n([\s\S]+?)\n```'
# Extract svelte block for App.svelte
svelte_match = re.search(svelte_pattern, text, re.IGNORECASE)
css_match = re.search(css_pattern, text, re.IGNORECASE)
if svelte_match:
files['src/App.svelte'] = svelte_match.group(1).strip()
if css_match:
files['src/app.css'] = css_match.group(1).strip()
# Fallback: support === filename === format if any file is missing
if not (files['src/App.svelte'] and files['src/app.css']):
# Use regex to extract sections
app_svelte_fallback = re.search(r'===\s*src/App\.svelte\s*===\n([\s\S]+?)(?=\n===|$)', text, re.IGNORECASE)
app_css_fallback = re.search(r'===\s*src/app\.css\s*===\n([\s\S]+?)(?=\n===|$)', text, re.IGNORECASE)
if app_svelte_fallback:
files['src/App.svelte'] = app_svelte_fallback.group(1).strip()
if app_css_fallback:
files['src/app.css'] = app_css_fallback.group(1).strip()
return files
def format_svelte_output(files):
"""Format Svelte files into a single display string"""
output = []
output.append("=== src/App.svelte ===")
output.append(files['src/App.svelte'])
output.append("\n=== src/app.css ===")
output.append(files['src/app.css'])
return '\n'.join(output)
def history_render(history: History):
return gr.update(visible=True), history
def clear_history():
return [], [], None, "" # Empty lists for both tuple format and chatbot messages, None for file, empty string for website URL
def update_image_input_visibility(model):
"""Update image input visibility based on selected model"""
is_ernie_vl = model.get("id") == "baidu/ERNIE-4.5-VL-424B-A47B-Base-PT"
is_glm_vl = model.get("id") == "THUDM/GLM-4.1V-9B-Thinking"
is_glm_45v = model.get("id") == "zai-org/GLM-4.5V"
return gr.update(visible=is_ernie_vl or is_glm_vl or is_glm_45v)
def process_image_for_model(image):
"""Convert image to base64 for model input"""
if image is None:
return None
# Convert numpy array to PIL Image if needed
import io
import base64
import numpy as np
from PIL import Image
# Handle numpy array from Gradio
if isinstance(image, np.ndarray):
image = Image.fromarray(image)
buffer = io.BytesIO()
image.save(buffer, format='PNG')
img_str = base64.b64encode(buffer.getvalue()).decode()
return f"data:image/png;base64,{img_str}"
def generate_image_with_qwen(prompt: str, image_index: int = 0) -> str:
"""Generate image using Qwen image model via Hugging Face InferenceClient with optimized data URL"""
try:
# Check if HF_TOKEN is available
if not os.getenv('HF_TOKEN'):
return "Error: HF_TOKEN environment variable is not set. Please set it to your Hugging Face API token."
# Create InferenceClient for Qwen image generation
client = InferenceClient(
provider="auto",
api_key=os.getenv('HF_TOKEN'),
bill_to="huggingface",
)
# Generate image using Qwen/Qwen-Image model
image = client.text_to_image(
prompt,
model="Qwen/Qwen-Image",
)
# Resize image to reduce size while maintaining quality
max_size = 512
if image.width > max_size or image.height > max_size:
image.thumbnail((max_size, max_size), Image.Resampling.LANCZOS)
# Convert PIL Image to optimized base64 for HTML embedding
import io
import base64
buffer = io.BytesIO()
# Save as JPEG with compression for smaller file size
image.convert('RGB').save(buffer, format='JPEG', quality=85, optimize=True)
img_str = base64.b64encode(buffer.getvalue()).decode()
# Return HTML img tag with optimized data URL
return f''
except Exception as e:
print(f"Image generation error: {str(e)}")
return f"Error generating image: {str(e)}"
def generate_image_to_image(input_image_data, prompt: str) -> str:
"""Generate an image using image-to-image with FLUX.1-Kontext-dev via Hugging Face InferenceClient.
Returns an HTML tag with optimized base64 JPEG data, similar to text-to-image output.
"""
try:
# Check token
if not os.getenv('HF_TOKEN'):
return "Error: HF_TOKEN environment variable is not set. Please set it to your Hugging Face API token."
# Prepare client
client = InferenceClient(
provider="auto",
api_key=os.getenv('HF_TOKEN'),
bill_to="huggingface",
)
# Normalize input image to bytes
import io
from PIL import Image
try:
import numpy as np
except Exception:
np = None
if hasattr(input_image_data, 'read'):
# File-like object
raw = input_image_data.read()
pil_image = Image.open(io.BytesIO(raw))
elif hasattr(input_image_data, 'mode') and hasattr(input_image_data, 'size'):
# PIL Image
pil_image = input_image_data
elif np is not None and isinstance(input_image_data, np.ndarray):
pil_image = Image.fromarray(input_image_data)
elif isinstance(input_image_data, (bytes, bytearray)):
pil_image = Image.open(io.BytesIO(input_image_data))
else:
# Fallback: try to convert via bytes
pil_image = Image.open(io.BytesIO(bytes(input_image_data)))
# Ensure RGB
if pil_image.mode != 'RGB':
pil_image = pil_image.convert('RGB')
buf = io.BytesIO()
pil_image.save(buf, format='PNG')
input_bytes = buf.getvalue()
# Call image-to-image
image = client.image_to_image(
input_bytes,
prompt=prompt,
model="black-forest-labs/FLUX.1-Kontext-dev",
)
# Resize/optimize
max_size = 512
if image.width > max_size or image.height > max_size:
image.thumbnail((max_size, max_size), Image.Resampling.LANCZOS)
out_buf = io.BytesIO()
image.convert('RGB').save(out_buf, format='JPEG', quality=85, optimize=True)
import base64
img_str = base64.b64encode(out_buf.getvalue()).decode()
return f""
except Exception as e:
print(f"Image-to-image generation error: {str(e)}")
return f"Error generating image (image-to-image): {str(e)}"
def extract_image_prompts_from_text(text: str, num_images_needed: int = 1) -> list:
"""Extract image generation prompts from the full text based on number of images needed"""
# Use the entire text as the base prompt for image generation
# Clean up the text and create variations for the required number of images
# Clean the text
cleaned_text = text.strip()
if not cleaned_text:
return []
# Create variations of the prompt for the required number of images
prompts = []
# Generate exactly the number of images needed
for i in range(num_images_needed):
if i == 0:
# First image: Use the full prompt as-is
prompts.append(cleaned_text)
elif i == 1:
# Second image: Add "visual representation" to make it more image-focused
prompts.append(f"Visual representation of {cleaned_text}")
elif i == 2:
# Third image: Add "illustration" to create a different style
prompts.append(f"Illustration of {cleaned_text}")
else:
# For additional images, use different variations
variations = [
f"Digital art of {cleaned_text}",
f"Modern design of {cleaned_text}",
f"Professional illustration of {cleaned_text}",
f"Clean design of {cleaned_text}",
f"Beautiful visualization of {cleaned_text}",
f"Stylish representation of {cleaned_text}",
f"Contemporary design of {cleaned_text}",
f"Elegant illustration of {cleaned_text}"
]
variation_index = (i - 3) % len(variations)
prompts.append(variations[variation_index])
return prompts
def create_image_replacement_blocks(html_content: str, user_prompt: str) -> str:
"""Create search/replace blocks to replace placeholder images with generated Qwen images"""
if not user_prompt:
return ""
# Find existing image placeholders in the HTML first
import re
# Common patterns for placeholder images
placeholder_patterns = [
r']*src=["\'](?:placeholder|dummy|sample|example)[^"\']*["\'][^>]*>',
r']*src=["\']https?://via\.placeholder\.com[^"\']*["\'][^>]*>',
r']*src=["\']https?://picsum\.photos[^"\']*["\'][^>]*>',
r']*src=["\']https?://dummyimage\.com[^"\']*["\'][^>]*>',
r']*alt=["\'][^"\']*placeholder[^"\']*["\'][^>]*>',
r']*class=["\'][^"\']*placeholder[^"\']*["\'][^>]*>',
r']*id=["\'][^"\']*placeholder[^"\']*["\'][^>]*>',
r']*src=["\']data:image[^"\']*["\'][^>]*>', # Base64 images
r']*src=["\']#["\'][^>]*>', # Empty src
r']*src=["\']about:blank["\'][^>]*>', # About blank
]
# Find all placeholder images
placeholder_images = []
for pattern in placeholder_patterns:
matches = re.findall(pattern, html_content, re.IGNORECASE)
placeholder_images.extend(matches)
# If no placeholder images found, look for any img tags
if not placeholder_images:
img_pattern = r']*>'
placeholder_images = re.findall(img_pattern, html_content)
# Also look for div elements that might be image placeholders
div_placeholder_patterns = [
r']*class=["\'][^"\']*(?:image|img|photo|picture)[^"\']*["\'][^>]*>.*?',
r']*id=["\'][^"\']*(?:image|img|photo|picture)[^"\']*["\'][^>]*>.*?',
]
for pattern in div_placeholder_patterns:
matches = re.findall(pattern, html_content, re.IGNORECASE | re.DOTALL)
placeholder_images.extend(matches)
# Count how many images we need to generate
num_images_needed = len(placeholder_images)
if num_images_needed == 0:
return ""
# Generate image prompts based on the number of images found
image_prompts = extract_image_prompts_from_text(user_prompt, num_images_needed)
# Generate images for each prompt
generated_images = []
for i, prompt in enumerate(image_prompts):
image_html = generate_image_with_qwen(prompt, i)
if not image_html.startswith("Error"):
generated_images.append((i, image_html))
if not generated_images:
return ""
# Create search/replace blocks
replacement_blocks = []
for i, (prompt_index, generated_image) in enumerate(generated_images):
if i < len(placeholder_images):
# Replace existing placeholder
placeholder = placeholder_images[i]
# Clean up the placeholder for better matching
placeholder_clean = re.sub(r'\s+', ' ', placeholder.strip())
# Try multiple variations of the placeholder for better matching
placeholder_variations = [
placeholder_clean,
placeholder_clean.replace('"', "'"),
placeholder_clean.replace("'", '"'),
re.sub(r'\s+', ' ', placeholder_clean),
placeholder_clean.replace(' ', ' '),
]
# Create a replacement block for each variation
for variation in placeholder_variations:
replacement_blocks.append(f"""{SEARCH_START}
{variation}
{DIVIDER}
{generated_image}
{REPLACE_END}""")
else:
# Add new image if we have more generated images than placeholders
# Find a good insertion point (after body tag or main content)
if '', html_content.find(' str:
"""Create search/replace blocks that generate and insert ONLY ONE text-to-image result.
Replaces the first detected placeholder; if none found, inserts one image near the top of .
"""
if not prompt or not prompt.strip():
return ""
import re
# Detect placeholders similarly to the multi-image version
placeholder_patterns = [
r']*src=["\'](?:placeholder|dummy|sample|example)[^"\']*["\'][^>]*>',
r']*src=["\']https?://via\.placeholder\.com[^"\']*["\'][^>]*>',
r']*src=["\']https?://picsum\.photos[^"\']*["\'][^>]*>',
r']*src=["\']https?://dummyimage\.com[^"\']*["\'][^>]*>',
r']*alt=["\'][^"\']*placeholder[^"\']*["\'][^>]*>',
r']*class=["\'][^"\']*placeholder[^"\']*["\'][^>]*>',
r']*id=["\'][^"\']*placeholder[^"\']*["\'][^>]*>',
r']*src=["\']data:image[^"\']*["\'][^>]*>',
r']*src=["\']#["\'][^>]*>',
r']*src=["\']about:blank["\'][^>]*>',
]
placeholder_images = []
for pattern in placeholder_patterns:
matches = re.findall(pattern, html_content, re.IGNORECASE)
if matches:
placeholder_images.extend(matches)
# Fallback to any if no placeholders
if not placeholder_images:
img_pattern = r']*>'
placeholder_images = re.findall(img_pattern, html_content)
# Generate a single image
image_html = generate_image_with_qwen(prompt, 0)
if image_html.startswith("Error"):
return ""
# Replace first placeholder if present
if placeholder_images:
placeholder = placeholder_images[0]
placeholder_clean = re.sub(r'\s+', ' ', placeholder.strip())
placeholder_variations = [
placeholder_clean,
placeholder_clean.replace('"', "'"),
placeholder_clean.replace("'", '"'),
re.sub(r'\s+', ' ', placeholder_clean),
placeholder_clean.replace(' ', ' '),
]
blocks = []
for variation in placeholder_variations:
blocks.append(f"""{SEARCH_START}
{variation}
{DIVIDER}
{image_html}
{REPLACE_END}""")
return '\n\n'.join(blocks)
# Otherwise insert after
if '', html_content.find(', just append
return f"{SEARCH_START}\n\n{DIVIDER}\n{image_html}\n{REPLACE_END}"
def create_image_replacement_blocks_from_input_image(html_content: str, user_prompt: str, input_image_data, max_images: int = 1) -> str:
"""Create search/replace blocks using image-to-image generation with a provided input image.
Mirrors placeholder detection from create_image_replacement_blocks but uses generate_image_to_image.
"""
if not user_prompt:
return ""
import re
placeholder_patterns = [
r']*src=["\'](?:placeholder|dummy|sample|example)[^"\']*["\'][^>]*>',
r']*src=["\']https?://via\.placeholder\.com[^"\']*["\'][^>]*>',
r']*src=["\']https?://picsum\.photos[^"\']*["\'][^>]*>',
r']*src=["\']https?://dummyimage\.com[^"\']*["\'][^>]*>',
r']*alt=["\'][^"\']*placeholder[^"\']*["\'][^>]*>',
r']*class=["\'][^"\']*placeholder[^"\']*["\'][^>]*>',
r']*id=["\'][^"\']*placeholder[^"\']*["\'][^>]*>',
r']*src=["\']data:image[^"\']*["\'][^>]*>',
r']*src=["\']#["\'][^>]*>',
r']*src=["\']about:blank["\'][^>]*>',
]
placeholder_images = []
for pattern in placeholder_patterns:
matches = re.findall(pattern, html_content, re.IGNORECASE)
placeholder_images.extend(matches)
if not placeholder_images:
img_pattern = r']*>'
placeholder_images = re.findall(img_pattern, html_content)
div_placeholder_patterns = [
r']*class=["\'][^"\']*(?:image|img|photo|picture)[^"\']*["\'][^>]*>.*?',
r']*id=["\'][^"\']*(?:image|img|photo|picture)[^"\']*["\'][^>]*>.*?',
]
for pattern in div_placeholder_patterns:
matches = re.findall(pattern, html_content, re.IGNORECASE | re.DOTALL)
placeholder_images.extend(matches)
num_images_needed = len(placeholder_images)
num_to_replace = min(num_images_needed, max(0, int(max_images)))
if num_images_needed == 0:
# No placeholders; generate one image to append (only if at least one upload is present)
if num_to_replace <= 0:
return ""
prompts = extract_image_prompts_from_text(user_prompt, 1)
if not prompts:
return ""
image_html = generate_image_to_image(input_image_data, prompts[0])
if image_html.startswith("Error"):
return ""
return f"{SEARCH_START}\n\n{DIVIDER}\n
{image_html}
\n{REPLACE_END}"
if num_to_replace <= 0:
return ""
image_prompts = extract_image_prompts_from_text(user_prompt, num_to_replace)
generated_images = []
for i, prompt in enumerate(image_prompts):
image_html = generate_image_to_image(input_image_data, prompt)
if not image_html.startswith("Error"):
generated_images.append((i, image_html))
if not generated_images:
return ""
replacement_blocks = []
for i, (prompt_index, generated_image) in enumerate(generated_images):
if i < num_to_replace and i < len(placeholder_images):
placeholder = placeholder_images[i]
placeholder_clean = re.sub(r'\s+', ' ', placeholder.strip())
placeholder_variations = [
placeholder_clean,
placeholder_clean.replace('"', "'"),
placeholder_clean.replace("'", '"'),
re.sub(r'\s+', ' ', placeholder_clean),
placeholder_clean.replace(' ', ' '),
]
for variation in placeholder_variations:
replacement_blocks.append(f"""{SEARCH_START}
{variation}
{DIVIDER}
{generated_image}
{REPLACE_END}""")
# Do not insert additional images beyond the uploaded count
return '\n\n'.join(replacement_blocks)
def apply_generated_images_to_html(html_content: str, user_prompt: str, enable_text_to_image: bool, enable_image_to_image: bool, input_image_data, image_to_image_prompt: str | None = None, text_to_image_prompt: str | None = None) -> str:
"""Apply text-to-image and/or image-to-image replacements to HTML content.
If both toggles are enabled, text-to-image replacements run first, then image-to-image.
"""
result = html_content
try:
# If an input image is provided and image-to-image is enabled, we only replace one image
# and skip text-to-image to satisfy the requirement to replace exactly the number of uploaded images.
if enable_image_to_image and input_image_data is not None and (result.strip().startswith('') or result.strip().startswith('') or result.strip().startswith(' str:
"""Apply search/replace changes to content (HTML, Python, etc.)"""
if not changes_text.strip():
return original_content
# Split the changes text into individual search/replace blocks
blocks = []
current_block = ""
lines = changes_text.split('\n')
for line in lines:
if line.strip() == SEARCH_START:
if current_block.strip():
blocks.append(current_block.strip())
current_block = line + '\n'
elif line.strip() == REPLACE_END:
current_block += line + '\n'
blocks.append(current_block.strip())
current_block = ""
else:
current_block += line + '\n'
if current_block.strip():
blocks.append(current_block.strip())
modified_content = original_content
for block in blocks:
if not block.strip():
continue
# Parse the search/replace block
lines = block.split('\n')
search_lines = []
replace_lines = []
in_search = False
in_replace = False
for line in lines:
if line.strip() == SEARCH_START:
in_search = True
in_replace = False
elif line.strip() == DIVIDER:
in_search = False
in_replace = True
elif line.strip() == REPLACE_END:
in_replace = False
elif in_search:
search_lines.append(line)
elif in_replace:
replace_lines.append(line)
# Apply the search/replace
if search_lines:
search_text = '\n'.join(search_lines).strip()
replace_text = '\n'.join(replace_lines).strip()
if search_text in modified_content:
modified_content = modified_content.replace(search_text, replace_text)
else:
print(f"Warning: Search text not found in content: {search_text[:100]}...")
return modified_content
def apply_transformers_js_search_replace_changes(original_formatted_content: str, changes_text: str) -> str:
"""Apply search/replace changes to transformers.js formatted content (three files)"""
if not changes_text.strip():
return original_formatted_content
# Parse the original formatted content to get the three files
files = parse_transformers_js_output(original_formatted_content)
# Split the changes text into individual search/replace blocks
blocks = []
current_block = ""
lines = changes_text.split('\n')
for line in lines:
if line.strip() == SEARCH_START:
if current_block.strip():
blocks.append(current_block.strip())
current_block = line + '\n'
elif line.strip() == REPLACE_END:
current_block += line + '\n'
blocks.append(current_block.strip())
current_block = ""
else:
current_block += line + '\n'
if current_block.strip():
blocks.append(current_block.strip())
# Process each block and apply changes to the appropriate file
for block in blocks:
if not block.strip():
continue
# Parse the search/replace block
lines = block.split('\n')
search_lines = []
replace_lines = []
in_search = False
in_replace = False
target_file = None
for line in lines:
if line.strip() == SEARCH_START:
in_search = True
in_replace = False
elif line.strip() == DIVIDER:
in_search = False
in_replace = True
elif line.strip() == REPLACE_END:
in_replace = False
elif in_search:
search_lines.append(line)
elif in_replace:
replace_lines.append(line)
# Determine which file this change targets based on the search content
if search_lines:
search_text = '\n'.join(search_lines).strip()
replace_text = '\n'.join(replace_lines).strip()
# Check which file contains the search text
if search_text in files['index.html']:
target_file = 'index.html'
elif search_text in files['index.js']:
target_file = 'index.js'
elif search_text in files['style.css']:
target_file = 'style.css'
# Apply the change to the target file
if target_file and search_text in files[target_file]:
files[target_file] = files[target_file].replace(search_text, replace_text)
else:
print(f"Warning: Search text not found in any transformers.js file: {search_text[:100]}...")
# Reformat the modified files
return format_transformers_js_output(files)
# Updated for faster Tavily search and closer prompt usage
# Uses 'advanced' search_depth and auto_parameters=True for speed and relevance
def perform_web_search(query: str, max_results: int = 5, include_domains=None, exclude_domains=None) -> str:
"""Perform web search using Tavily with default parameters"""
if not tavily_client:
return "Web search is not available. Please set the TAVILY_API_KEY environment variable."
try:
# Use Tavily defaults with advanced search depth for better results
search_params = {
"search_depth": "advanced",
"max_results": min(max(1, max_results), 20)
}
if include_domains is not None:
search_params["include_domains"] = include_domains
if exclude_domains is not None:
search_params["exclude_domains"] = exclude_domains
response = tavily_client.search(query, **search_params)
search_results = []
for result in response.get('results', []):
title = result.get('title', 'No title')
url = result.get('url', 'No URL')
content = result.get('content', 'No content')
search_results.append(f"Title: {title}\nURL: {url}\nContent: {content}\n")
if search_results:
return "Web Search Results:\n\n" + "\n---\n".join(search_results)
else:
return "No search results found."
except Exception as e:
return f"Search error: {str(e)}"
def enhance_query_with_search(query: str, enable_search: bool) -> str:
"""Enhance the query with web search results if search is enabled"""
if not enable_search or not tavily_client:
return query
# Perform search to get relevant information
search_results = perform_web_search(query)
# Combine original query with search results
enhanced_query = f"""Original Query: {query}
{search_results}
Please use the search results above to help create the requested application with the most up-to-date information and best practices."""
return enhanced_query
def send_to_sandbox(code):
"""Render HTML in a sandboxed iframe. Assumes full HTML is provided by prompts."""
html_doc = (code or "").strip()
encoded_html = base64.b64encode(html_doc.encode('utf-8')).decode('utf-8')
data_uri = f"data:text/html;charset=utf-8;base64,{encoded_html}"
iframe = f''
return iframe
def is_streamlit_code(code: str) -> bool:
"""Heuristic check to determine if Python code is a Streamlit app."""
if not code:
return False
lowered = code.lower()
return ("import streamlit" in lowered) or ("from streamlit" in lowered) or ("st." in code and "streamlit" in lowered)
def send_streamlit_to_stlite(code: str) -> str:
"""Render Streamlit code using stlite inside a sandboxed iframe for preview."""
# Build an HTML document that loads stlite and mounts the Streamlit app defined inline
html_doc = (
"""
Streamlit Preview
"""
+ (code or "")
+ """
"""
)
encoded_html = base64.b64encode(html_doc.encode('utf-8')).decode('utf-8')
data_uri = f"data:text/html;charset=utf-8;base64,{encoded_html}"
iframe = f''
return iframe
def is_gradio_code(code: str) -> bool:
"""Heuristic check to determine if Python code is a Gradio app."""
if not code:
return False
lowered = code.lower()
return (
"import gradio" in lowered
or "from gradio" in lowered
or "gr.Interface(" in code
or "gr.Blocks(" in code
)
def send_gradio_to_lite(code: str) -> str:
"""Render Gradio code using gradio-lite inside a sandboxed iframe for preview."""
html_doc = (
"""
Gradio Preview
"""
+ (code or "")
+ """
"""
)
encoded_html = base64.b64encode(html_doc.encode('utf-8')).decode('utf-8')
data_uri = f"data:text/html;charset=utf-8;base64,{encoded_html}"
iframe = f''
return iframe
def demo_card_click(e: gr.EventData):
try:
# Get the index from the event data
if hasattr(e, '_data') and e._data:
# Try different ways to get the index
if 'index' in e._data:
index = e._data['index']
elif 'component' in e._data and 'index' in e._data['component']:
index = e._data['component']['index']
elif 'target' in e._data and 'index' in e._data['target']:
index = e._data['target']['index']
else:
# If we can't get the index, try to extract it from the card data
index = 0
else:
index = 0
# Ensure index is within bounds
if index >= len(DEMO_LIST):
index = 0
return DEMO_LIST[index]['description']
except (KeyError, IndexError, AttributeError) as e:
# Return the first demo description as fallback
return DEMO_LIST[0]['description']
def extract_text_from_image(image_path):
"""Extract text from image using OCR"""
try:
# Check if tesseract is available
try:
pytesseract.get_tesseract_version()
except Exception:
return "Error: Tesseract OCR is not installed. Please install Tesseract to extract text from images. See install_tesseract.md for instructions."
# Read image using OpenCV
image = cv2.imread(image_path)
if image is None:
return "Error: Could not read image file"
# Convert to RGB (OpenCV uses BGR)
image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Preprocess image for better OCR results
# Convert to grayscale
gray = cv2.cvtColor(image_rgb, cv2.COLOR_RGB2GRAY)
# Apply thresholding to get binary image
_, binary = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
# Extract text using pytesseract
text = pytesseract.image_to_string(binary, config='--psm 6')
return text.strip() if text.strip() else "No text found in image"
except Exception as e:
return f"Error extracting text from image: {e}"
def extract_text_from_file(file_path):
if not file_path:
return ""
mime, _ = mimetypes.guess_type(file_path)
ext = os.path.splitext(file_path)[1].lower()
try:
if ext == ".pdf":
with open(file_path, "rb") as f:
reader = PyPDF2.PdfReader(f)
return "\n".join(page.extract_text() or "" for page in reader.pages)
elif ext in [".txt", ".md"]:
with open(file_path, "r", encoding="utf-8") as f:
return f.read()
elif ext == ".csv":
with open(file_path, "r", encoding="utf-8") as f:
return f.read()
elif ext == ".docx":
doc = docx.Document(file_path)
return "\n".join([para.text for para in doc.paragraphs])
elif ext.lower() in [".jpg", ".jpeg", ".png", ".bmp", ".tiff", ".tif", ".gif", ".webp"]:
return extract_text_from_image(file_path)
else:
return ""
except Exception as e:
return f"Error extracting text: {e}"
def extract_website_content(url: str) -> str:
"""Extract HTML code and content from a website URL"""
try:
# Validate URL
parsed_url = urlparse(url)
if not parsed_url.scheme:
url = "https://" + url
parsed_url = urlparse(url)
if not parsed_url.netloc:
return "Error: Invalid URL provided"
# Set comprehensive headers to mimic a real browser request
headers = {
'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36',
'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8',
'Accept-Language': 'en-US,en;q=0.9',
'Accept-Encoding': 'gzip, deflate, br',
'DNT': '1',
'Connection': 'keep-alive',
'Upgrade-Insecure-Requests': '1',
'Sec-Fetch-Dest': 'document',
'Sec-Fetch-Mode': 'navigate',
'Sec-Fetch-Site': 'none',
'Sec-Fetch-User': '?1',
'Cache-Control': 'max-age=0'
}
# Create a session to maintain cookies and handle redirects
session = requests.Session()
session.headers.update(headers)
# Make the request with retry logic
max_retries = 3
for attempt in range(max_retries):
try:
response = session.get(url, timeout=15, allow_redirects=True)
response.raise_for_status()
break
except requests.exceptions.HTTPError as e:
if e.response.status_code == 403 and attempt < max_retries - 1:
# Try with different User-Agent on 403
session.headers['User-Agent'] = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36'
continue
else:
raise
# Get the raw HTML content with proper encoding
try:
# Try to get the content with automatic encoding detection
response.encoding = response.apparent_encoding
raw_html = response.text
except:
# Fallback to UTF-8 if encoding detection fails
raw_html = response.content.decode('utf-8', errors='ignore')
# Debug: Check if we got valid HTML
if not raw_html.strip().startswith(' 10:
print(f"Warning: This site has {len(script_tags)} script tags - it may be a JavaScript-heavy site")
print("The content might be loaded dynamically and not available in the initial HTML")
# Extract title
title = soup.find('title')
title_text = title.get_text().strip() if title else "No title found"
# Extract meta description
meta_desc = soup.find('meta', attrs={'name': 'description'})
description = meta_desc.get('content', '') if meta_desc else ""
# Extract main content areas for analysis
content_sections = []
main_selectors = [
'main', 'article', '.content', '.main-content', '.post-content',
'#content', '#main', '.entry-content', '.post-body'
]
for selector in main_selectors:
elements = soup.select(selector)
for element in elements:
text = element.get_text().strip()
if len(text) > 100: # Only include substantial content
content_sections.append(text)
# Extract navigation links for analysis
nav_links = []
nav_elements = soup.find_all(['nav', 'header'])
for nav in nav_elements:
links = nav.find_all('a')
for link in links:
link_text = link.get_text().strip()
link_href = link.get('href', '')
if link_text and link_href:
nav_links.append(f"{link_text}: {link_href}")
# Extract and fix image URLs in the HTML
img_elements = soup.find_all('img')
for img in img_elements:
src = img.get('src', '')
if src:
# Handle different URL formats
if src.startswith('//'):
# Protocol-relative URL
absolute_src = 'https:' + src
img['src'] = absolute_src
elif src.startswith('/'):
# Root-relative URL
absolute_src = urljoin(url, src)
img['src'] = absolute_src
elif not src.startswith(('http://', 'https://')):
# Relative URL
absolute_src = urljoin(url, src)
img['src'] = absolute_src
# If it's already absolute, keep it as is
# Also check for data-src (lazy loading) and other common attributes
data_src = img.get('data-src', '')
if data_src and not src:
# Use data-src if src is empty
if data_src.startswith('//'):
absolute_data_src = 'https:' + data_src
img['src'] = absolute_data_src
elif data_src.startswith('/'):
absolute_data_src = urljoin(url, data_src)
img['src'] = absolute_data_src
elif not data_src.startswith(('http://', 'https://')):
absolute_data_src = urljoin(url, data_src)
img['src'] = absolute_data_src
else:
img['src'] = data_src
# Also fix background image URLs in style attributes
elements_with_style = soup.find_all(attrs={'style': True})
for element in elements_with_style:
style_attr = element.get('style', '')
# Find and replace relative URLs in background-image
import re
bg_pattern = r'background-image:\s*url\(["\']?([^"\']+)["\']?\)'
matches = re.findall(bg_pattern, style_attr, re.IGNORECASE)
for match in matches:
if match:
if match.startswith('//'):
absolute_bg = 'https:' + match
style_attr = style_attr.replace(match, absolute_bg)
elif match.startswith('/'):
absolute_bg = urljoin(url, match)
style_attr = style_attr.replace(match, absolute_bg)
elif not match.startswith(('http://', 'https://')):
absolute_bg = urljoin(url, match)
style_attr = style_attr.replace(match, absolute_bg)
element['style'] = style_attr
# Fix background images in
'
except Exception as e:
print(f"Image generation error: {str(e)}")
return f"Error generating image: {str(e)}"
def generate_image_to_image(input_image_data, prompt: str) -> str:
"""Generate an image using image-to-image with FLUX.1-Kontext-dev via Hugging Face InferenceClient.
Returns an HTML