Update app.py

app.py

@@ -108,58 +108,55 @@ def reset_upload():
         flash('No file to remove.')    
     return redirect(url_for('index'))
 
-@app.route('/process_file/<filename>', methods=['GET', 'POST'])
-def process_file(filename):    
+@app.route('/process', methods=['GET','POST'])
+def process_file():
     uploaded_files = session.get('uploaded_files', [])
     if not uploaded_files:
-        print('No files selected for processing')
+        flash('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}")
+
+        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...")
-        # 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}")
-        
+        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')
+    flash('Data processed and analyzed successfully')
     logging.info("Data processed and analyzed successfully")
     return redirect(url_for('result'))