Update app.py

app.py

@@ -72,7 +72,7 @@ def upload_file():
     session['uploaded_files'] = uploaded_files  
     flash('Files successfully uploaded')
     logging.info(f"Files successfully uploaded: {uploaded_files}")
-    return process_file()
+    return process_file(uploaded_files)
 
 @app.route('/remove_file',methods=['POST'])
 def remove_file():
@@ -108,56 +108,71 @@ def reset_upload():
         flash('No file to remove.')    
     return redirect(url_for('index'))
 
-@app.route('/process', methods=['GET','POST'])
-def process_file():
-    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'))
+@app.route('/process_file/<filename>', methods=['GET', 'POST'])
+def process_file(filename):
+    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}")
+
+            #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")
 
-    file_paths = [os.path.join(app.config['UPLOAD_FOLDER'], filename) for filename in uploaded_files]
-    logging.info(f"Processing files: {file_paths}")
+        # Process extracted text and structure the output
+        cont_data = process_extracted_text(extracted_text)
+        logging.info(f"Contextual data: {cont_data}")
 
-    extracted_text = {}
-    processed_Img = {}
+        processed_data = process_resume_data(LLMdata, cont_data, extracted_text)
+        logging.info(f"Processed data: {processed_data}")
 
-    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 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}")
-
-    session['processed_data'] = processed_data
-    session['processed_Img'] = processed_Img
-    flash('Data processed and analyzed successfully')
-    logging.info("Data processed and analyzed successfully")
+        # 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')