Update app.py to show confidence scores for genres

app.py

@@ -1,66 +1,85 @@
-import gradio as gr
-import pickle
-import os
-
-# Define paths to the pickle files
-BASE_DIR = os.path.dirname(os.path.abspath(__file__))
-PKL_DIR = os.path.join(BASE_DIR, 'pkl_files')
-
-SENTIMENT_MODEL_PATH = os.path.join(PKL_DIR, 'sentiment_model.pkl')
-SENTIMENT_VECTORIZER_PATH = os.path.join(PKL_DIR, 'sentiment_vectorizer.pkl')
-GENRE_MODEL_PATH = os.path.join(PKL_DIR, 'genre_model.pkl')
-GENRE_VECTORIZER_PATH = os.path.join(PKL_DIR, 'genre_vectorizer.pkl')
-
-# Load models and vectorizers
-def load_pickle(path):
-    with open(path, 'rb') as f:
-        return pickle.load(f)
-
-print("Loading models...")
-try:
-    sentiment_model = load_pickle(SENTIMENT_MODEL_PATH)
-    sentiment_vectorizer = load_pickle(SENTIMENT_VECTORIZER_PATH)
-    genre_model = load_pickle(GENRE_MODEL_PATH)
-    genre_vectorizer = load_pickle(GENRE_VECTORIZER_PATH)
-    print("Models loaded successfully.")
-except Exception as e:
-    print(f"Error loading models: {e}")
-    raise e
-
-def predict_review(review_text):
-    if not review_text:
-        return "Please enter a review.", "Please enter a review."
-    
-    # Sentiment Prediction
-    try:
-        # Transform text using the sentiment vectorizer
-        # Note: transform expects an iterable, so we wrap review_text in a list
-        sentiment_features = sentiment_vectorizer.transform([review_text])
-        sentiment_prediction = sentiment_model.predict(sentiment_features)[0]
-    except Exception as e:
-        sentiment_prediction = f"Error in sentiment prediction: {str(e)}"
-
-    # Genre Prediction
-    try:
-        # Transform text using the genre vectorizer
-        genre_features = genre_vectorizer.transform([review_text])
-        genre_prediction = genre_model.predict(genre_features)[0]
-    except Exception as e:
-        genre_prediction = f"Error in genre prediction: {str(e)}"
-
-    return sentiment_prediction, genre_prediction
-
-# Create Gradio Interface
-iface = gr.Interface(
-    fn=predict_review,
-    inputs=gr.Textbox(lines=5, placeholder="Enter movie review here...", label="Movie Review"),
-    outputs=[
-        gr.Textbox(label="Predicted Sentiment"),
-        gr.Textbox(label="Predicted Genre")
-    ],
-    title="Movie Review Sentiment & Genre Classifier",
-    description="Enter a movie review to predict its sentiment and the movie genre based on the text."
-)
-
-if __name__ == "__main__":
-    iface.launch()
+import gradio as gr
+import pickle
+import os
+import numpy as np
+
+# Define paths to the pickle files
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+PKL_DIR = os.path.join(BASE_DIR, 'pkl_files')
+
+SENTIMENT_MODEL_PATH = os.path.join(PKL_DIR, 'sentiment_model.pkl')
+SENTIMENT_VECTORIZER_PATH = os.path.join(PKL_DIR, 'sentiment_vectorizer.pkl')
+GENRE_MODEL_PATH = os.path.join(PKL_DIR, 'genre_model.pkl')
+GENRE_VECTORIZER_PATH = os.path.join(PKL_DIR, 'genre_vectorizer.pkl')
+
+# Load models and vectorizers
+def load_pickle(path):
+    with open(path, 'rb') as f:
+        return pickle.load(f)
+
+print("Loading models...")
+try:
+    sentiment_model = load_pickle(SENTIMENT_MODEL_PATH)
+    sentiment_vectorizer = load_pickle(SENTIMENT_VECTORIZER_PATH)
+    genre_model = load_pickle(GENRE_MODEL_PATH)
+    genre_vectorizer = load_pickle(GENRE_VECTORIZER_PATH)
+    print("Models loaded successfully.")
+except Exception as e:
+    print(f"Error loading models: {e}")
+    raise e
+
+def predict_review(review_text):
+    if not review_text:
+        return "Please enter a review.", "Please enter a review."
+    
+    # Sentiment Prediction
+    try:
+        # Transform text using the sentiment vectorizer
+        # Note: transform expects an iterable, so we wrap review_text in a list
+        sentiment_features = sentiment_vectorizer.transform([review_text])
+        sentiment_prediction = sentiment_model.predict(sentiment_features)[0]
+    except Exception as e:
+        sentiment_prediction = f"Error in sentiment prediction: {str(e)}"
+
+    # Genre Prediction
+    try:
+        # Transform text using the genre vectorizer
+        genre_features = genre_vectorizer.transform([review_text])
+        
+        # Use decision_function to get confidence scores
+        if hasattr(genre_model, "decision_function"):
+            decision_scores = genre_model.decision_function(genre_features)[0]
+            
+            # Apply Softmax to convert scores to probabilities
+            exp_scores = np.exp(decision_scores)
+            probabilities = exp_scores / np.sum(exp_scores)
+            
+            # Map classes to probabilities
+            genre_prediction = {
+                label: float(prob) 
+                for label, prob in zip(genre_model.classes_, probabilities)
+            }
+        else:
+            # Fallback if decision_function is not available
+            pred = genre_model.predict(genre_features)[0]
+            genre_prediction = {pred: 1.0}
+            
+    except Exception as e:
+        genre_prediction = {"Error": str(e)}
+
+    return sentiment_prediction, genre_prediction
+
+# Create Gradio Interface
+iface = gr.Interface(
+    fn=predict_review,
+    inputs=gr.Textbox(lines=5, placeholder="Enter movie review here...", label="Movie Review"),
+    outputs=[
+        gr.Textbox(label="Predicted Sentiment"),
+        gr.Label(label="Predicted Genre", num_top_classes=3)
+    ],
+    title="Movie Review Sentiment & Genre Classifier",
+    description="Enter a movie review to predict its sentiment and the movie genre based on the text."
+)
+
+if __name__ == "__main__":
+    iface.launch()