Update app.py

app.py

@@ -24,6 +24,8 @@ model = AutoModel.from_pretrained("Alibaba-NLP/gte-multilingual-base", trust_rem
 def load_dataset(file_path='PEC_Numbers_and_Names.xlsx'):
     if os.path.exists(file_path):
         df = pd.read_excel(file_path)
+        print("File loaded successfully.")
+        print(df.head())  # Print first few rows for debugging
     else:
         raise FileNotFoundError(f"File not found: {file_path}")
     return df
@@ -31,13 +33,16 @@ def load_dataset(file_path='PEC_Numbers_and_Names.xlsx'):
 # Function to get the name based on the PEC number
 def get_name(pec_number, df):
     df['PEC No.'] = df['PEC No.'].str.strip().str.upper()
-    pec_number = pec_number.strip().str.upper()
+    pec_number = pec_number.strip().upper()
     
+    print(f"Searching for PEC Number: {pec_number}")  # Debugging output
     result = df[df['PEC No.'] == pec_number]
     
     if not result.empty:
+        print(f"Found Name: {result.iloc[0]['Name']}")  # Debugging output
         return result.iloc[0]['Name']
     else:
+        print("PEC Number not found.")  # Debugging output
         return "PEC Number not found."
 
 # Function to process the PEC number using the Hugging Face model
@@ -56,8 +61,9 @@ def predict(pec_number):
         name = get_name(pec_number, df)
         model_output = process_with_model(pec_number)
         return f"Name: {name}\nModel Output: {model_output}"
-    except FileNotFoundError as e:
-        return str(e)
+    except Exception as e:
+        print(f"An error occurred: {e}")
+        return f"Error: {e}"
 
 # Build the Gradio interface without the file upload option
 iface = gr.Interface(
@@ -71,3 +77,4 @@ iface = gr.Interface(
 # Run the Gradio interface
 if __name__ == "__main__":
     iface.launch()
+