Lsai2122/dev

1import gradio as gr2import pandas as pd3# NOTE: These import paths rely on the structure you had before.4from endpoints.models.indicbert import analyze_sentiments5from endpoints.models.summarization_flan25.main import generate_aggregated_summary6 7# --- Core Logic Wrappers ---8 9def get_sentiment_gradio(comments_text: str) -> pd.DataFrame:10    """11    Takes text input (comments separated by newlines) and runs sentiment analysis.12    Safely handles percentage values that might be returned as strings or non-numeric types.13    """14    # 1. Parse Input: Split the single string into a list of comments15    comments = [c.strip() for c in comments_text.split('\n') if c.strip()]16    17    if not comments:18        # Return an empty DataFrame for empty input19        return pd.DataFrame({'Comment': [], 'Sentiment': [], 'Confidence (%)': []})20 21    try:22        # 2. Run the core logic23        # results and percentages are returned by analyze_sentiments24        results, percentages = analyze_sentiments(comments)25        26        # 3. Safely format percentages27        formatted_percentages = []28        for p in percentages:29            try:30                # Attempt to convert to float. Treat empty/None value as 0.031                value = float(p) if p else 0.032                # Format to two decimal places and append the percentage symbol33                formatted_percentages.append(f"{value * 100:.2f}%")34            except ValueError:35                # If conversion fails (e.g., "N/A", "Error"), use a placeholder36                formatted_percentages.append("N/A")37 38        # 4. Build the DataFrame39        data = {40            "Comment": comments,41            "Sentiment": results,42            "Confidence (%)": formatted_percentages 43        }44        return pd.DataFrame(data)45    46    except Exception as e:47        # Catch all other potential errors (like ImportErrors or model crashes)48        raise gr.Error(f"Sentiment analysis failed: {str(e)}")49 50 51def get_summary_gradio(comments_text: str) -> str:52    """53    Takes text input (comments separated by newlines) and returns a single summary string.54    """55    # 1. Parse Input: Split the single string into a list of comments56    comments = [c.strip() for c in comments_text.split('\n') if c.strip()]57    58    if not comments:59        return "Please enter comments to generate a summary."60 61    try:62        # 2. Run the core logic63        result = generate_aggregated_summary(comments)64        65        # 3. Format Output: Return the summary string66        return result67        68    except Exception as e:69        raise gr.Error(f"Summary generation failed: {str(e)}")70 71 72# --- Gradio Interface Setup ---73 74# Tab 1: Sentiment Analysis (Index 0, accessible via /run/predict)75sentiment_interface = gr.Interface(76    fn=get_sentiment_gradio,77    inputs=gr.Textbox(78        lines=10, 79        label="Enter Comments (one per line)",80        value="This product is excellent and arrived quickly.\nI am disappointed with the customer service.\nIt's fine, nothing special either way."81    ),82    outputs=gr.Dataframe(83        headers=["Comment", "Sentiment", "Confidence (%)"], 84        col_count=(3, "fixed"), 85        label="Sentiment Analysis Results"86    ),87    title="Sentiment Analysis (IndicBERT)",88    description="Analyze the sentiment of multiple comments and view the confidence score."89)90 91# Tab 2: Summarization (Index 1, accessible via /run/predict_1)92summary_interface = gr.Interface(93    fn=get_summary_gradio,94    inputs=gr.Textbox(95        lines=10, 96        label="Enter Comments for Aggregated Summary (one per line)",97        value="The battery life is amazing, lasting two full days.\nThe camera is poor in low light conditions.\nThe screen quality is top-notch, very bright and clear."98    ),99    outputs=gr.Textbox(100        label="Aggregated Summary",101        lines=5102    ),103    title="Aggregated Summarization (FLAN-T5)",104    description="Generate a single summary covering all points from the input comments."105)106 107# Combine interfaces into a Tabbed Interface108demo = gr.TabbedInterface([sentiment_interface, summary_interface], ["Sentiment Analysis", "Summarization"])109 110if __name__ == "__main__":111    demo.launch(server_name="0.0.0.0", server_port=7860, share=False, ssr_mode=False)