fix: resolve merge conflicts and update UI

app.py

@@ -11,18 +11,22 @@ import logging
 import cv2
 from datetime import datetime
 import time
+import os
 
 # Set up logging for error handling
 logging.basicConfig(level=logging.DEBUG)
+logger = logging.getLogger(__name__)
 
 # Initialize the OCR reader
 reader = easyocr.Reader(['en'])
 
-# Use text classification model (distilbert for sentiment analysis or text validation)
-text_classifier = pipeline("text-classification", model="distilbert-base-uncased-finetuned-sst-2-english")
-
-# Use document classification model (ResNet50 as an example)
-doc_classifier = pipeline("image-classification", model="microsoft/resnet-50")
+# Initialize models with error handling
+try:
+    text_classifier = pipeline("text-classification", model="distilbert-base-uncased-finetuned-sst-2-english")
+    doc_classifier = pipeline("image-classification", model="microsoft/resnet-50")
+except Exception as e:
+    logger.error(f"Error initializing models: {str(e)}")
+    raise
 
 def convert_pdf_to_images(pdf_file):
     """Convert PDF to list of images with detailed logging"""
@@ -96,93 +100,96 @@ def process_single_image(image):
         logging.error(f"Error processing the image: {str(e)}")
         return {'error': f"Error processing the image: {str(e)}"}
 
-# Gradio interface setup
-def gradio_interface(input_file):
-    """Handle both PDF and image files uploaded by the user"""
+def generate_excel_report(result):
+    """Generate Excel report from processing results"""
+    try:
+        df = pd.DataFrame([{
+            'Timestamp': datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
+            'Extracted Text': result.get('text', ''),
+            'Validation': result.get('validation', ''),
+            'Validation Confidence': result.get('validation_confidence', 0),
+            'Document Type': result.get('doc_type', ''),
+            'Document Confidence': result.get('doc_confidence', 0)
+        }])
+        
+        output_path = f"report_{datetime.now().strftime('%Y%m%d_%H%M%S')}.xlsx"
+        df.to_excel(output_path, index=False)
+        return output_path
+    except Exception as e:
+        logger.error(f"Error generating report: {str(e)}")
+        return None
+
+def process_claim(input_file):
+    """Main processing function for the Gradio interface"""
     try:
+        if input_file is None:
+            return "Please upload a file.", "No file provided.", "No file provided.", None
+
+        # Process the file
         if input_file.name.lower().endswith('.pdf'):
             images = convert_pdf_to_images(input_file)
             if images is None:
-                return {'error': 'Invalid PDF or unable to extract images'}
+                return "Error processing PDF.", "Invalid PDF file.", "Processing failed.", None
             result = process_single_image(images[0])
-        elif input_file.name.lower().endswith(('png', 'jpg', 'jpeg')):
+        elif input_file.name.lower().endswith(('png', 'jpg', 'jpeg', 'tiff')):
             img = Image.open(input_file)
             result = process_single_image(img)
         else:
-            return {'error': 'Unsupported file type. Please upload a valid image or PDF.'}
+            return "Unsupported file type.", "Invalid file format.", "Processing failed.", None
+
+        if 'error' in result:
+            return result['error'], "Processing failed.", "Processing failed.", None
 
-        return result
+        # Generate Excel report
+        report_path = generate_excel_report(result)
+        
+        # Format output strings
+        validation_text = f"Validation: {result['validation']}\nConfidence: {result['validation_confidence']:.2%}"
+        classification_text = f"Type: {result['doc_type']}\nConfidence: {result['doc_confidence']:.2%}"
+
+        return (
+            result['text'],
+            validation_text,
+            classification_text,
+            report_path if report_path else None
+        )
 
     except Exception as e:
-        logging.error(f"Error in file processing: {str(e)}")
-        return {'error': f"Error processing the file: {str(e)}"}
+        logger.error(f"Error in process_claim: {str(e)}")
+        return str(e), "Processing failed.", "Processing failed.", None
 
-# Create the Gradio interface with improved UI and error handling
+# Create the Gradio interface
 iface = gr.Interface(
-    fn=gradio_interface,
+    fn=process_claim,
     inputs=[
         gr.File(
             label="Upload Insurance Document",
-            file_types=["pdf", "png", "jpg", "jpeg", "tiff"],
-            type="filepath"
+            file_types=["pdf", "png", "jpg", "jpeg", "tiff"]
         )
     ],
     outputs=[
-        gr.Textbox(
-            label="Extracted Text",
-            lines=10,
-            show_copy_button=True
-        ),
-        gr.Textbox(
-            label="Validation Results",
-            lines=3,
-            show_copy_button=True
-        ),
-        gr.Textbox(
-            label="Document Classification",
-            lines=3,
-            show_copy_button=True
-        ),
-        gr.File(
-            label="Download Analysis Report (Excel)",
-            type="filepath"
-        )
+        gr.Textbox(label="Extracted Text", lines=10),
+        gr.Textbox(label="Validation Results", lines=3),
+        gr.Textbox(label="Document Classification", lines=3),
+        gr.File(label="Download Analysis Report")
     ],
     title="Insurance Claim Document Analyzer",
     description="Upload insurance documents (PDF/Images) for automated text extraction, validation, and classification.",
-    article="""
-    ### Supported Features:
-    - Multi-page PDF processing
-    - Image formats: PNG, JPG, TIFF
-    - Text extraction & validation
-    - Document classification
-    - Detailed Excel report generation
-    
-    ### File Requirements:
-    - Maximum file size: 10MB
-    - Clear, readable content
-    - English language documents
-    """,
-    examples=[],
-    cache_examples=False,
-    theme=gr.themes.Soft(
-        primary_hue="blue",
-        secondary_hue="gray",
-        neutral_hue="gray"
-    ),
-    css=".gradio-container {max-width: 900px; margin: auto}",
-    allow_flagging="never",
-    analytics_enabled=False
+    theme=gr.themes.Soft(primary_hue="blue"),
+    css=".gradio-container {max-width: 900px; margin: auto}"
 )
 
-# Launch with custom configurations
+# Launch with error handling
 if __name__ == "__main__":
-    iface.launch(
-        server_name="0.0.0.0",
-        server_port=7860,
-        share=False,
-        debug=True,
-        enable_queue=True,
-        show_error=True,
-        max_threads=4
-    )
+    try:
+        iface.launch(
+            server_name="0.0.0.0",
+            server_port=7860,
+            share=False,
+            debug=True,
+            enable_queue=True,
+            max_threads=4
+        )
+    except Exception as e:
+        logger.error(f"Failed to launch interface: {str(e)}")
+        raise