Update app.py

app.py

@@ -18,6 +18,8 @@ import torch
 
 # Load the dataset containing PEC numbers and names
 def load_dataset(file_path='PEC_Numbers_and_Names.xlsx'):
+    if not os.path.exists(file_path):
+        raise FileNotFoundError(f"File not found: {file_path}")
     df = pd.read_excel(file_path)
     return df
 
@@ -38,17 +40,17 @@ def process_with_model(pec_number):
     inputs = tokenizer(pec_number, return_tensors="pt")
     with torch.no_grad():
         outputs = model(**inputs)
-    # Return a simple representation of the model's output
     return outputs.last_hidden_state.mean(dim=1).squeeze().tolist()
 
 # Combine both functions to create a prediction
 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}"
-
-# Load the dataset
-df = load_dataset()
+    try:
+        df = load_dataset()
+        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)
 
 # Build the Gradio interface
 iface = gr.Interface(