app.py

import gradio as gr
import pandas as pd
import torch
from extract_text import extract_text_from_image
from text_similarity import analyze_similarity

def process_image(image, key_texts, similarity_threshold, fragment_threshold):
    """Processes the image, extracts text, and analyzes similarities."""
    try:
        if image is None:
            return "Please upload an image for analysis.", None, None, None, None, None
        
        if not key_texts.strip():
            return "Please enter key texts for comparison.", None, None, None, None, None
        
        # Extract text from the image using the user's method
        gpu_available = torch.cuda.is_available()
        extracted_texts = extract_text_from_image(image, gpu_available)
    
        if isinstance(key_texts, str):
            key_texts = [text.strip() for text in key_texts.split('\n') if text.strip()]
        
        # Process the analysis
        results = analyze_similarity(
            extracted_texts,
            key_texts,
            similarity_threshold=similarity_threshold/100,  # Convert percentage to decimal
            fragment_threshold=fragment_threshold/100  # Convert percentage to decimal
        )
        
        # Gerar relatório HTML
        html_report = generate_html_report(results)
        
        # Gerar DataFrames
        dfs = generate_results_dataframe(results)
        
        # Extrair DataFrames individuais (ou criar vazios se não existirem)
        df_statistics = dfs.get("statistics", pd.DataFrame())
        df_similar = dfs.get("similar", pd.DataFrame(columns=["Index", "Original Text", "Key Text", "Similarity"]))
        df_fragments = dfs.get("fragments", pd.DataFrame(columns=["Index", "Original Text", "Key Text", "Similarity"]))
        df_combined = dfs.get("combined", pd.DataFrame(columns=["Indices", "Text 1", "Text 2", "Combined Text", "Key Text", "Similarity"]))
    
        return html_report, df_statistics, df_similar, df_fragments, df_combined, extracted_texts, gpu_available

    
    except Exception as e:
        return f"Erro ao processar: {str(e)}", None, None, None, None, None
    
def process_manual_input(texts, key_texts, similarity_threshold, fragment_threshold):
    """Processes the user's manual text input."""
    # Validate input
    if not texts.strip() or not key_texts.strip():
        return "Please enter texts for analysis and key texts for comparison.", None, None, None, None
    
    try:
        # Process the analysis
        results = analyze_similarity(
            texts,
            key_texts,
            similarity_threshold=similarity_threshold/100,  # Convert percentage to decimal
            fragment_threshold=fragment_threshold/100  # Convert percentage to decimal
        )
        
        # Generate HTML report
        html_report = generate_html_report(results)
        
        # Gerar DataFrames
        dfs = generate_results_dataframe(results)
        
        # Extract individual DataFrames (or create empty ones if they don't exist)
        df_statistics = dfs.get("statistics", pd.DataFrame())
        df_similar = dfs.get("similar", pd.DataFrame(columns=["Index", "Original Text", "Key Text", "Similarity"]))
        df_fragments = dfs.get("fragments", pd.DataFrame(columns=["Index", "Original Text", "Key Text", "Similarity"]))
        df_combined = dfs.get("combined", pd.DataFrame(columns=["Indices", "Text 1", "Text 2", "Combined Text", "Key Text", "Similarity"]))
        
        return html_report, df_statistics, df_similar, df_fragments, df_combined
    
    except Exception as e:
        return f"Erro ao processar: {str(e)}", None, None, None, None

def generate_html_report(results):
    """Generates an HTML report about the detected similarities."""
    html = "<h2>Similarity Report</h2>"
    
    # General statistics
    html += "<div padding: 15px; border-radius: 5px; margin-bottom: 20px;'>"
    html += f"<p><b>Total texts analyzed:</b> {results['statistics']['total_analyzed']}</p>"
    html += f"<p><b>Texts with detected similarity:</b> {results['statistics']['total_processed']}</p>"
    html += "</div>"
    
     # Results table
    html += "<h3>Detected Similarities</h3>"
    
    # Similar texts
    if results["similar_texts"]:
        html += "<h4>Direct Similar Texts</h4>"
        html += "<table width='100%' style='border-collapse: collapse; margin-bottom: 20px;'>"
        html += "<tr><th style='text-align: left; padding: 8px; border: 1px solid #ddd;'>Original Text</th><th style='text-align: left; padding: 8px; border: 1px solid #ddd;'>Key Text</th><th style='text-align: center; padding: 8px; border: 1px solid #ddd;'>Similarity</th></tr>"
        
        for item in results["similar_texts"]:
            html += f"<tr><td style='padding: 8px; border: 1px solid #ddd;'>{item['text']}</td><td style='padding: 8px; border: 1px solid #ddd;'>{item['key_text']}</td><td style='text-align: center; padding: 8px; border: 1px solid #ddd;'>{item['similarity']:.2%}</td></tr>"
        
        html += "</table>"
    
    # Detected fragments
    if results["fragments_detected"]:
        html += "<h4>Text with Detected Fragments</h4>"
        html += "<table width='100%' style='border-collapse: collapse; margin-bottom: 20px;'>"
        html += "<tr><th style='text-align: left; padding: 8px; border: 1px solid #ddd;'>Original Text</th><th style='text-align: left; padding: 8px; border: 1px solid #ddd;'>Key Text</th><th style='text-align: center; padding: 8px; border: 1px solid #ddd;'>Similarity</th></tr>"
        
        for item in results["fragments_detected"]:
            html += f"<tr><td style='padding: 8px; border: 1px solid #ddd;'>{item['text']}</td><td style='padding: 8px; border: 1px solid #ddd;'>{item['key_text']}</td><td style='text-align: center; padding: 8px; border: 1px solid #ddd;'>{item['similarity']:.2%}</td></tr>"
        
        html += "</table>"
    
    # Combined texts
    if results["combined"]:
        html += "<h4>Text that need to be combined</h4>"
        html += "<table width='100%' style='border-collapse: collapse; margin-bottom: 20px;'>"
        html += "<tr><th style='text-align: left; padding: 8px; border: 1px solid #ddd;'>Text 1</th><th style='text-align: left; padding: 8px; border: 1px solid #ddd;'>Text 2</th><th style='text-align: left; padding: 8px; border: 1px solid #ddd;'>Combination</th><th style='text-align: left; padding: 8px; border: 1px solid #ddd;'>Key Text</th><th style='text-align: center; padding: 8px; border: 1px solid #ddd;'>Similarity</th></tr>"
        
        for item in results["combined"]:
            html += f"<tr><td style='padding: 8px; border: 1px solid #ddd;'>{item['texts'][0]}</td><td style='padding: 8px; border: 1px solid #ddd;'>{item['texts'][1]}</td><td style='padding: 8px; border: 1px solid #ddd;'>{item['combined_text']}</td><td style='padding: 8px; border: 1px solid #ddd;'>{item['key_text']}</td><td style='text-align: center; padding: 8px; border: 1px solid #ddd;'>{item['similarity']:.2%}</td></tr>"
        
        html += "</table>"
    
    if not (results["similar_texts"] or results["fragments_detected"] or results["combined"]):
        html += "<p>No significant similarity found with the current parameters.</p>"
    
    return html

def generate_results_dataframe(results):
    """Generates pandas DataFrames to visualize the results."""
    dfs = {}
    
    # DataFrame for similar texts
    if results["similar_texts"]:
        data = [(item['index'], item['text'], item['key_text'], f"{item['similarity']:.2%}") 
                for item in results["similar_texts"]]
        dfs["similar"] = pd.DataFrame(data, columns=["Index", "Original Text", "Key Text", "Similarity"])
    
    # DataFrame for fragments
    if results["fragments_detected"]:
        data = [(item['index'], item['text'], item['key_text'], f"{item['similarity']:.2%}") 
                for item in results["fragments_detected"]]
        dfs["fragments"] = pd.DataFrame(data, columns=["Index", "Original Text", "Key Text", "Similarity"])
    
    # DataFrame for combined
    if results["combined"]:
        data = [(f"{item['indices'][0]},{item['indices'][1]}", 
                  item['texts'][0], 
                  item['texts'][1],
                  item['combined_text'],
                  item['key_text'], 
                  f"{item['similarity']:.2%}") 
                for item in results["combined"]]
        dfs["combined"] = pd.DataFrame(data, columns=["Indices", "Text 1", "Text 2", 
                                                        "Combined Text", "Key Text", "Similarity"])
    
    # Statistics DataFrame
    data = [
        ("Total analyzed", results["statistics"]["total_analyzed"]),
        ("Total with similarity", results["statistics"]["total_processed"]),
        ("Direct similarity", results["statistics"]["direct_similarity"]),
        ("Fragments", results["statistics"]["fragments"]),
        ("Combined", results["statistics"]["combined"])
    ]
    dfs["statistics"] = pd.DataFrame(data, columns=["Metric", "Value"])
    
    return dfs

def generate_gradio():
    with gr.Blocks(title="Text Similarity Detector") as demo:
        gr.Markdown("# 🔍 Text Similarity Detector with Image Extraction")
        gr.Markdown("""
        This tool analyzes the similarity between texts extracted from an image and reference key texts.
        It can identify:
        - Direct similar texts
        - Key text fragments within the texts
        - Text combinations that match key texts
        """)

        with gr.Tabs() as tabs:
            with gr.TabItem("Image Analysis"):
                with gr.Row():
                    with gr.Column(scale=1):  # Column for inputs on the left
                        input_image = gr.Image(label="Upload an image to extract text", type="pil", height=600)
                        key_texts_image = gr.Textbox(
                                label="Key Texts for Comparison",
                                placeholder="Paste your key texts here (one per line)",
                                lines=5
                            )
                        # with gr.Row():
                        #     key_texts_image = gr.Textbox(
                        #         label="Key Texts for Comparison",
                        #         placeholder="Paste your key texts here (one per line)",
                        #         lines=5
                        #     )

                        #     min_similarity_per_key_image = gr.Textbox(
                        #         label="Minimum Similarity for Each Key Text (%)",
                        #         placeholder="Enter one value per line, matching the key texts",
                        #         lines=5
                        #     )

                        with gr.Row():
                            similarity_threshold_image = gr.Slider(
                                label="Similarity Threshold (%)",
                                minimum=50,
                                maximum=100,
                                value=70,
                                step=1
                            )
                            fragment_threshold_image = gr.Slider(
                                label="Fragment Similarity Threshold (%)",
                                minimum=50,
                                maximum=100,
                                value=70,
                                step=1
                            )

                        analyze_image_btn = gr.Button("Analyze Image", variant="primary")
                    
                    with gr.Column(scale=1):  # Column for outputs on the right
                        gpu_available = gr.Checkbox(label="Used GPU")
                        extracted_texts = gr.Textbox(label="Extracted Texts from the Image", lines=5)
                        html_output = gr.HTML(label="Similarity Report")
                        with gr.Tabs():
                            with gr.TabItem("Statistics"):
                                statistics_output = gr.Dataframe(label="Statistics")
                            with gr.TabItem("Direct Similarity"):
                                similar_texts_output = gr.Dataframe(label="Direct Similar Texts")
                            with gr.TabItem("Fragments"):
                                fragments_output = gr.Dataframe(label="Texts with Fragments")
                            with gr.TabItem("Combined"):
                                combined_output = gr.Dataframe(label="Combined Texts")

            with gr.TabItem("Manual Analysis"):
                with gr.Row():
                    with gr.Column(scale=1):  # Column for inputs on the left
                        input_texts = gr.Textbox(
                            label="List of Texts for Analysis",
                            placeholder="Paste your list of texts here (one per line)",
                            lines=10
                        )
                        key_texts_input = gr.Textbox(
                                label="Key Texts for Comparison",
                                placeholder="Paste your key texts here (one per line)",
                                lines=5
                            )
                        # with gr.Row():
                        #     key_texts_input = gr.Textbox(
                        #         label="Key Texts for Comparison",
                        #         placeholder="Paste your key texts here (one per line)",
                        #         lines=5
                        #     )

                        #     min_similarity_per_key_input = gr.Textbox(
                        #         label="Minimum Similarity for Each Key Text (%)",
                        #         placeholder="Enter one value per line, matching the key texts",
                        #         lines=5
                        #     )
                                        
                        with gr.Row():
                            similarity_threshold = gr.Slider(
                                label="Similarity Threshold (%)",
                                minimum=50,
                                maximum=100,
                                value=70,
                                step=1
                            )
                            fragment_threshold = gr.Slider(
                                label="Fragment Similarity Threshold (%)",
                                minimum=50,
                                maximum=100,
                                value=70,
                                step=1
                            )

                        analyze_btn = gr.Button("Analyze Image", variant="primary")

                    with gr.Column(scale=1):  # Column for outputs on the right
                        html_output_manual = gr.HTML(label="Manual Similarity Report")
                        with gr.Tabs():
                            with gr.TabItem("Statistics"):
                                statistics_output_manual = gr.Dataframe(label="Statistics")
                            with gr.TabItem("Direct Similarity"):
                                similar_texts_output_manual = gr.Dataframe(label="Direct Similar Texts")
                            with gr.TabItem("Fragments"):
                                fragments_output_manual = gr.Dataframe(label="Texts with Fragments")
                            with gr.TabItem("Combined"):
                                combined_output_manual = gr.Dataframe(label="Combined Texts")

        # Connect the image processing function to the button
        analyze_image_btn.click(
            process_image,
            inputs=[input_image, key_texts_image, similarity_threshold_image, fragment_threshold_image],
            outputs=[html_output, statistics_output, similar_texts_output, fragments_output, combined_output, extracted_texts, gpu_available]
        )

        # Connect the manual text processing function to the button
        analyze_btn.click(
            process_manual_input,
            inputs=[input_texts, key_texts_input, similarity_threshold, fragment_threshold],
            outputs=[html_output_manual, statistics_output_manual, similar_texts_output_manual, fragments_output_manual, combined_output_manual]
        )
    
    return demo

#app = gr.mount_gradio_app(app, demo, path="/")

if __name__ == "__main__":
    generate_gradio.launch()

# PORT = int(os.getenv("PORT", 7860))

# if __name__ == "__main__":
#     import uvicorn
#     print(f"A arrancar na porta {PORT}...")
#     uvicorn.run(app)

    #demo.launch(server_name="0.0.0.0", server_port=7860)