feat: Add error handling to display runtime exceptions in UI

app.py

@@ -1,23 +1,21 @@
 # Import the necessary libraries
 import gradio as gr
 from transformers import pipeline
+import traceback # Import the traceback module to get detailed errors
 
 # --- Configuration ---
 # Define the models we want to offer in our application.
-# We are now using a confirmed, publicly available Persian model.
 MODELS = {
     "English": "distilbert-base-uncased-finetuned-sst-2-english",
     "Persian (Farsi)": "m3hrdadfi/albert-fa-base-v2-sentiment-binary",
 }
 
 # --- State and Caching ---
-# Create a dictionary to cache the loaded pipelines.
 pipeline_cache = {}
 
 def get_pipeline(model_name):
     """
     Loads a sentiment-analysis pipeline for the given model name.
-    Uses a cache to avoid reloading models that are already in memory.
     """
     if model_name not in pipeline_cache:
         print(f"Loading pipeline for model: {model_name}...")
@@ -25,22 +23,30 @@ def get_pipeline(model_name):
         print("Pipeline loaded successfully.")
     return pipeline_cache[model_name]
 
-# --- Core Logic ---
+# --- Core Logic with Error Handling ---
 def analyze_sentiment(text, model_choice):
     """
     Analyzes the sentiment of a given text using the selected model.
+    Includes a try-except block to catch and display any errors.
     """
-    if not text:
-        return "Please enter some text to analyze."
-    
-    model_name = MODELS[model_choice]
-    sentiment_pipeline = get_pipeline(model_name)
-    
-    result = sentiment_pipeline(text)[0]
-    label = result['label']
-    score = result['score']
-    
-    return f"Sentiment: {label} (Score: {score:.4f})"
+    try:
+        if not text:
+            return "Please enter some text to analyze."
+        
+        model_name = MODELS[model_choice]
+        sentiment_pipeline = get_pipeline(model_name)
+        
+        result = sentiment_pipeline(text)[0]
+        label = result['label']
+        score = result['score']
+        
+        return f"Sentiment: {label} (Score: {score:.4f})"
+    except Exception as e:
+        # If any error occurs, format it and return it to the UI.
+        # This is our debugging tool!
+        error_details = traceback.format_exc()
+        print(error_details) # Also print to server logs if we can see them
+        return f"An error occurred:\n\n{error_details}"
 
 # --- Gradio Interface ---
 with gr.Blocks() as iface:
@@ -53,7 +59,7 @@ with gr.Blocks() as iface:
             value="English", 
             label="Select Language Model"
         )
-        output_text = gr.Textbox(label="Result", interactive=False)
+        output_text = gr.Textbox(label="Result", interactive=False, lines=10) # Made the box bigger for errors
 
     input_text = gr.Textbox(lines=5, placeholder="Enter text here...")
     submit_button = gr.Button("Analyze Sentiment")