Update app.py

app.py

@@ -109,72 +109,60 @@ def reset_upload():
     return redirect(url_for('index'))
 
 @app.route('/process_file/<filename>', methods=['GET', 'POST'])
-def process_file(filename):
+def process_file(filename):    
+    uploaded_files = session.get('uploaded_files', [])
+    if not uploaded_files:
+        print('No files selected for processing')
+        logging.warning("No files selected for processing")
+        return redirect(url_for('index'))
+    # 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:
-        uploaded_files = session.get('uploaded_files', [])
-        if not uploaded_files:
-            print('No files selected for processing')
-            logging.warning("No files selected for processing")
-            return redirect(url_for('index'))
-
-        # 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}")
-
+        extracted_text, processed_Img = extract_text_from_images(file_paths)
+        logging.info(f"Extracted text: {extracted_text}")
+        logging.info(f"Processed images: {processed_Img}")
+        #run the model code only if the text is extracted.
+        if extracted_text:
+            llmText = json_to_llm_str(extracted_text)
+            logging.info(f"LLM text: {llmText}")
             #run the model code only if the text is extracted.
-            if extracted_text:
-                llmText = json_to_llm_str(extracted_text)
-                logging.info(f"LLM text: {llmText}")
-
-                #run the model code only if the text is extracted.
-                LLMdata = Data_Extractor(llmText)
-                print("Json Output from model------------>",LLMdata)
-                logging.info(f"LLM data: {LLMdata}")
-            else:
-                raise ('The text is not detected in the OCR')
-
-        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)
-                print("Json Output from model------------>",LLMdata)
-                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
-
-        print('Data processed and analyzed successfully')
-        logging.info("Data processed and analyzed successfully")
-        return redirect(url_for('result'))
-
+            LLMdata = Data_Extractor(llmText)
+            print("Json Output from model------------>",LLMdata)
+            logging.info(f"LLM data: {LLMdata}")
+        else:
+            raise ('The text is not detected in the OCR')
+    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)
+            print("Json Output from model------------>",LLMdata)
+            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
+    print('Data processed and analyzed successfully')
+    logging.info("Data processed and analyzed successfully")
+    return redirect(url_for('result'))
+    
 @app.route('/result')
 def result():
     processed_data = session.get('processed_data', {})