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import gradio as gr
import logging
from config import config
from app import SentimentApp
# Optimized Gradio Interface
def create_interface():
"""Create comprehensive Gradio interface with optimizations"""
app = SentimentApp()
with gr.Blocks(theme=gr.themes.Soft(), title="Multilingual Sentiment Analyzer") as demo:
gr.Markdown("# 🌍 Multilingual Sentiment Analyzer")
gr.Markdown("AI-powered sentiment analysis with SHAP & LIME explainable AI features")
with gr.Tab("Single Analysis"):
with gr.Row():
with gr.Column():
text_input = gr.Textbox(
label="Enter Text for Analysis",
placeholder="Enter your text in any supported language...",
lines=5
)
with gr.Row():
language_selector = gr.Dropdown(
choices=list(config.SUPPORTED_LANGUAGES.values()),
value="Auto Detect",
label="Language"
)
theme_selector = gr.Dropdown(
choices=list(config.THEMES.keys()),
value="default",
label="Theme"
)
with gr.Row():
clean_text_cb = gr.Checkbox(label="Clean Text", value=False)
remove_punct_cb = gr.Checkbox(label="Remove Punctuation", value=False)
remove_nums_cb = gr.Checkbox(label="Remove Numbers", value=False)
analyze_btn = gr.Button("Analyze", variant="primary", size="lg")
gr.Examples(
examples=app.examples,
inputs=text_input,
cache_examples=False
)
with gr.Column():
result_output = gr.Textbox(label="Analysis Results", lines=8)
with gr.Row():
gauge_plot = gr.Plot(label="Sentiment Gauge")
probability_plot = gr.Plot(label="Probability Distribution")
# FIXED Advanced Analysis Tab
with gr.Tab("Advanced Analysis"):
gr.Markdown("## Explainable AI Analysis")
gr.Markdown("**SHAP and LIME analysis with FIXED implementation** - now handles text input correctly!")
with gr.Row():
with gr.Column():
advanced_text_input = gr.Textbox(
label="Enter Text for Advanced Analysis",
placeholder="Enter text to analyze with SHAP and LIME...",
lines=6,
value="This movie is absolutely fantastic and amazing!"
)
with gr.Row():
advanced_language = gr.Dropdown(
choices=list(config.SUPPORTED_LANGUAGES.values()),
value="Auto Detect",
label="Language"
)
num_samples_slider = gr.Slider(
minimum=50,
maximum=300,
value=100,
step=25,
label="Number of Samples",
info="Lower = Faster, Higher = More Accurate"
)
with gr.Row():
shap_btn = gr.Button("SHAP Analysis", variant="primary")
lime_btn = gr.Button("LIME Analysis", variant="secondary")
gr.Markdown("""
**📊 Analysis Methods:**
- **SHAP**: Token-level importance scores using Text masker
- **LIME**: Feature importance through text perturbation
**⚡ Expected Performance:**
- 50 samples: ~10-20s | 100 samples: ~20-40s | 200+ samples: ~40-80s
""")
with gr.Column():
advanced_results = gr.Textbox(label="Analysis Summary", lines=12)
with gr.Row():
advanced_plot = gr.Plot(label="Feature Importance Visualization")
with gr.Tab("Batch Analysis"):
with gr.Row():
with gr.Column():
file_upload = gr.File(
label="Upload File (CSV/TXT)",
file_types=[".csv", ".txt"]
)
batch_input = gr.Textbox(
label="Batch Input (one text per line)",
placeholder="Enter multiple texts, one per line...",
lines=10
)
with gr.Row():
batch_language = gr.Dropdown(
choices=list(config.SUPPORTED_LANGUAGES.values()),
value="Auto Detect",
label="Language"
)
batch_theme = gr.Dropdown(
choices=list(config.THEMES.keys()),
value="default",
label="Theme"
)
with gr.Row():
batch_clean_cb = gr.Checkbox(label="Clean Text", value=False)
batch_punct_cb = gr.Checkbox(label="Remove Punctuation", value=False)
batch_nums_cb = gr.Checkbox(label="Remove Numbers", value=False) |