Update app.py

app.py

@@ -55,32 +55,47 @@ def index():
 
 @app.route('/upload', methods=['POST'])
 def upload_file():
-    if 'files' not in request.files:
-        flash('No file part')
-        logging.warning("No file part found in the request")
-        return redirect(request.url)
+    try:
+        # Check if 'files' part is in the request
+        if 'files' not in request.files:
+            flash('No file part')
+            logging.warning("No file part found in the request")
+            return redirect(request.url)
+
+        files = request.files.getlist('files')  # Get all files from the request
+        if not files or all(file.filename == '' for file in files):
+            flash('No selected files')
+            logging.warning("No files selected for upload")
+            return jsonify({'message': "No files selected for upload"})
+
+        uploaded_files = session.get('uploaded_files', [])
+        for file in files:
+            if file:
+                filename = file.filename
+                file_path = os.path.join(app.config['UPLOAD_FOLDER'], filename)
+                
+                # Save the file
+                try:
+                    file.save(file_path)
+                    uploaded_files.append(filename)
+                    logging.info(f"Uploaded file: {filename} at {file_path}")
+                except Exception as save_error:
+                    logging.error(f"Error saving file {filename}: {save_error}")
+                    return jsonify({'message': f"Error saving file {filename}"}), 500
+
+        session['uploaded_files'] = uploaded_files  # Store the uploaded files in session
+        flash('Files successfully uploaded')
+        logging.info(f"Files successfully uploaded: {uploaded_files}")
+        return jsonify({
+            'message': 'Files successfully uploaded',
+            'uploaded_files': uploaded_files
+        })
 
-    files = request.files.getlist('files')  
-    if not files or all(file.filename == '' for file in files):
-        flash('No selected files')
-        logging.warning("No files selected for upload")
-        return redirect(request.url)
+    except Exception as e:
+        logging.error(f"An error occurred during file upload: {e}")
+        flash('An error occurred during file upload')
+        return jsonify({'message': 'File upload failed'}), 500
 
-    uploaded_files = session.get('uploaded_files', [])
-    for file in files:
-        if file:
-            filename = file.filename
-            file_path = os.path.join(app.config['UPLOAD_FOLDER'], filename)
-            file.save(file_path)
-            uploaded_files.append(filename)
-            logging.info(f"Uploaded file: {filename} at {file_path}")
-
-    session['uploaded_files'] = uploaded_files  
-    flash('Files successfully uploaded')
-    logging.info(f"Files successfully uploaded: {uploaded_files}")
-    return jsonify({'filename':filename,
-                    'filepath':file_path
-                   })
 
 @app.route('/remove_file')
 def remove_file():
@@ -102,62 +117,71 @@ from flask import jsonify
 
 @app.route('/process/<filename>', methods=['GET', 'POST'])
 def process_file(filename):
-    uploaded_files = session.get('uploaded_files', [])
-    if not uploaded_files:
-        flash('No files selected for processing')
-        logging.warning("No files selected for processing")
-        return redirect(url_for('index'))
-    
-    BASE_DIR = 'uploads/'
-
-    # Joining the base and the requested path
-    #file_paths = os.path.join(BASE_DIR, filename)
-
-    file_paths = [os.path.join(app.config['UPLOAD_FOLDER'], filename) for filename in uploaded_files]
-    logging.info(f"Processing files: {file_paths}")
-
-    extracted_text = {}
-    processed_Img = {}
-
     try:
-        extracted_text, processed_Img = extract_text_from_images(file_paths)
-        logging.info(f"Extracted text: {extracted_text}")
-        logging.info(f"Processed images: {processed_Img}")
-
-        llmText = json_to_llm_str(extracted_text)
-        logging.info(f"LLM text: {llmText}")
-        
-        LLMdata = Data_Extractor(llmText)
-        logging.info(f"LLM data: {LLMdata}")
+        uploaded_files = session.get('uploaded_files', [])
+        if not uploaded_files:
+            flash('No files selected for processing')
+            logging.warning("No files selected for processing")
+            return redirect(url_for('index'))
+
+        BASE_DIR = 'uploads/'
+
+        # Joining the base and the requested path
+        file_paths = [os.path.join(app.config['UPLOAD_FOLDER'], filename) for filename in uploaded_files]
+        logging.info(f"Processing files: {file_paths}")
+
+        extracted_text = {}
+        processed_Img = {}
+
+        # Try to process using the main model (Mistral 7b)
+        try:
+            extracted_text, processed_Img = extract_text_from_images(file_paths)
+            logging.info(f"Extracted text: {extracted_text}")
+            logging.info(f"Processed images: {processed_Img}")
+
+            llmText = json_to_llm_str(extracted_text)
+            logging.info(f"LLM text: {llmText}")
+            
+            LLMdata = Data_Extractor(llmText)
+            logging.info(f"LLM data: {LLMdata}")
+
+        except Exception as model_error:
+            logging.error(f"Error during LLM processing: {model_error}")
+            logging.info("Running backup model...")
+
+            # Use backup model in case of errors
+            LLMdata = {}
+            extracted_text, processed_Img = extract_text_from_images(file_paths)
+            logging.info(f"Extracted text (Backup): {extracted_text}")
+            logging.info(f"Processed images (Backup): {processed_Img}")
+            
+            if extracted_text:
+                text = json_to_llm_str(extracted_text)
+                LLMdata = NER_Model(text)
+                logging.info(f"NER model data: {LLMdata}")
+            else:
+                logging.warning("No extracted text available for backup model")
+
+        # Process extracted text and structure the output
+        cont_data = process_extracted_text(extracted_text)
+        logging.info(f"Contextual data: {cont_data}")
+
+        processed_data = process_resume_data(LLMdata, cont_data, extracted_text)
+        logging.info(f"Processed data: {processed_data}")
+
+        # Save data in session for later use
+        session['processed_data'] = processed_data
+        session['processed_Img'] = processed_Img
+
+        flash('Data processed and analyzed successfully')
+        logging.info("Data processed and analyzed successfully")
+        return jsonify({'process_data': [processed_data], 'process_image': processed_Img})
 
     except Exception as e:
-        logging.error(f"Error during LLM processing: {e}")
-        logging.info("Running backup model...")
-
-        LLMdata = {}
-        extracted_text, processed_Img = extract_text_from_images(file_paths)
-        logging.info(f"Extracted text(Backup): {extracted_text}")
-        logging.info(f"Processed images(Backup): {processed_Img}")
-        if extracted_text:
-            text = json_to_llm_str(extracted_text)
-            LLMdata = NER_Model(text)
-            logging.info(f"NER model data: {LLMdata}")
-        else:
-            logging.warning("No extracted text available for backup model")
-
-    cont_data = process_extracted_text(extracted_text)
-    logging.info(f"Contextual data: {cont_data}")
-
-    processed_data = process_resume_data(LLMdata, cont_data, extracted_text)
-    logging.info(f"Processed data: {processed_data}")
+        logging.error(f"An unexpected error occurred during file processing: {e}")
+        flash('An error occurred during file processing')
+        return jsonify({'message': 'File processing failed'}), 500
 
-    session['processed_data'] = processed_data
-    session['processed_Img'] = processed_Img
-    flash('Data processed and analyzed successfully')
-    logging.info("Data processed and analyzed successfully")
-    return jsonify({'process_data': [processed_data],
-                    'process_image': processed_Img
-                   })
 
 @app.route('/result')
 def result():