Update app.py

app.py

@@ -1,38 +1,56 @@
-# app.py
-import gradio as gr
-import joblib
-
-# --- 1. Load the Model and Vectorizer ---
-# Load the trained model and the TF-IDF vectorizer from disk.
-try:
-    model = joblib.load('logistic_regression_model.joblib')
-    vectorizer = joblib.load('tfidf_vectorizer.joblib')
-    print("Model and vectorizer loaded successfully.")
-except FileNotFoundError:
-    print("Error: Model or vectorizer files not found. Make sure they are in the same directory.")
-    # We'll let the app crash if files aren't found, as it can't run without them.
-    raise
-
-# --- 2. Define the Prediction Function ---
-# This function will take a text input and return the predicted sentiment.
-def predict_sentiment(text):
-    # Transform the input text using the loaded vectorizer.
-    vectorized_text = vectorizer.transform([text])
-    # Make a prediction using the loaded model.
-    prediction = model.predict(vectorized_text)
-    # Return the first element of the prediction array.
-    return prediction[0]
-
-# --- 3. Create and Launch the Gradio Interface ---
-# Define the user interface for the app.
-iface = gr.Interface(
-    fn=predict_sentiment,
-    inputs=gr.Textbox(lines=5, label="Enter a sentence to classify"),
-    outputs=gr.Label(label="Predicted Sentiment"),
-    title="Simple Sentiment Analysis",
-    description="A simple sentiment analysis model that classifies text as positive, negative, or neutral (depending on your training).",
-    allow_flagging="never"
-)
-
-# Launch the app. This will start a web server.
-iface.launch()
+# app.py (with human-readable labels)
+import gradio as gr
+import joblib
+
+# --- Load Model and Vectorizer ---
+try:
+    model = joblib.load('logistic_regression_model.joblib')
+    vectorizer = joblib.load('tfidf_vectorizer.joblib')
+    print("✅ Model and vectorizer loaded successfully!")
+except Exception as e:
+    print(f"🛑 Error loading files: {e}")
+    model, vectorizer = None, None
+
+# --- Define Prediction Logic with Label Mapping ---
+def predict_sentiment(text):
+    if not model or not vectorizer:
+        return "ERROR: Model is not loaded. Check terminal logs."
+    
+    try:
+        # 1. Transform the input text
+        vectorized_text = vectorizer.transform([text])
+        
+        # 2. Make a numerical prediction
+        prediction = model.predict(vectorized_text)
+        numeric_prediction = prediction[0]
+        
+        # --- NEW CODE: Map prediction to labels ---
+        # 3. Define the mapping from numbers to labels
+        sentiment_map = {
+            0: "Neutral 😐",
+            1: "Happy 😊",
+            2: "Sad 😞"
+        }
+        
+        # 4. Get the label from the map.
+        #    The .get() method safely returns a default value if the key isn't found.
+        sentiment_label = sentiment_map.get(numeric_prediction, "Unknown Prediction")
+        
+        # 5. Return the final human-readable label
+        return sentiment_label
+        
+    except Exception as e:
+        print(f"--- PREDICTION ERROR --- \n{e}\n --- END ---")
+        return f"An error occurred during prediction: {e}"
+
+# --- Create and Launch the Gradio Interface ---
+iface = gr.Interface(
+    fn=predict_sentiment,
+    inputs=gr.Textbox(lines=5, label="Enter a Sentence"),
+    outputs=gr.Label(label="Predicted Sentiment"),
+    title="Sentiment Analysis Model",
+    description="Analyzes text and classifies it as Happy, Sad, or Neutral."
+)
+
+print("🚀 Launching Gradio app...")
+iface.launch()