Update app.py

app.py

@@ -164,6 +164,13 @@ def process_file():
         logging.info(f"Extracted text blobs: {extracted_text}")
         logging.info(f"Processed images: {processed_Img}")
 
+        # If LLMdata is essentially empty (all values are empty lists), we might want to try backup
+        is_empty = all(len(v) == 0 for k, v in LLMdata.items() if k != 'extracted_text')
+        
+        if is_empty:
+            logging.info("Groq VLM returned empty data. Trying backup model...")
+            raise ValueError("Empty data from Groq VLM")
+
         # Regex fallback / augmentation from model text
         cont_data = process_extracted_text(extracted_text)
         logging.info(f"Contextual data: {cont_data}")
@@ -178,26 +185,28 @@ def process_file():
         return redirect(url_for('result'))
 
     except Exception as e:
-        logging.exception(f"Error during processing: {e}")
-        flash('Processing failed')
+        logging.exception(f"Error during primary processing: {e}")
+        flash('Primary processing failed, attempting backup model...')
 
-        LLMdata = {}
-        extracted_text, processed_Img = extract_text_from_images(file_paths)
+        # Correct unpacking (3 values)
+        LLMdata_v1, 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}")
+        
+        LLMdata = {}
         try:
             if extracted_text:
-                
-                    text = json_to_llm_str(extracted_text)
-                    LLMdata = NER_Model(text)
-                    logging.info(f"NER model data: {LLMdata}")
+                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")
-        except Exception as e:
-            logging.exception(f"Error during processing: {e}")
-            flash('Processing failed')
+        except Exception as backup_e:
+            logging.exception(f"Error during backup processing: {backup_e}")
+            flash('Backup processing also failed')
             return redirect(url_for('index'))
 
+    # Final merge using backup data if we reached here
     cont_data = process_extracted_text(extracted_text)
     logging.info(f"Contextual data: {cont_data}")
 
@@ -206,9 +215,10 @@ def process_file():
 
     session['processed_data'] = processed_data
     session['processed_Img'] = processed_Img
-    flash('Data processed and analyzed successfully')
-    logging.info("Data processed and analyzed successfully")
+    flash('Data processed using backup model')
+    logging.info("Data processed using backup model")
     return redirect(url_for('result'))
+
     
 @app.route('/result')
 def result():
@@ -224,4 +234,4 @@ def uploaded_file(filename):
 
 if __name__ == '__main__':
     logging.info("Starting Flask app")
-    app.run(debug=True)
+    app.run(debug=True)