Update app.py

app.py

@@ -1,15 +1,12 @@
-import streamlit as st
+import gradio as gr
 import pickle
 import re
 import nltk
 from nltk.corpus import stopwords
 
 # Download stopwords if not already present
-try:
-    stop_words = set(stopwords.words('english'))
-except LookupError:
-    nltk.download('stopwords')
-    stop_words = set(stopwords.words('english'))
+nltk.download('stopwords', quiet=True)
+stop_words = set(stopwords.words('english'))
 
 def clean_text(text):
     """Clean and preprocess text similar to training preprocessing"""
@@ -20,75 +17,34 @@ def clean_text(text):
     words = [w for w in words if w not in stop_words]  # Remove stopwords
     return ' '.join(words)  # Join words back
 
-@st.cache_resource
-def load_model_and_vectorizer():
-    """Load the trained model and vectorizer"""
+# Load model and vectorizer
+with open("model.pkl", "rb") as f:
+    model = pickle.load(f)
+with open("vectorizer.pkl", "rb") as f:
+    vectorizer = pickle.load(f)
+
+def analyze_review(review_text):
+    if not review_text.strip():
+        return "⚠️ Please enter a review."
     try:
-        with open("model.pkl", "rb") as f:
-            model = pickle.load(f)
-        with open("vectorizer.pkl", "rb") as f:
-            vectorizer = pickle.load(f)
-        return model, vectorizer
-    except FileNotFoundError as e:
-        st.error(f"Model files not found: {e}")
-        st.stop()
+        cleaned_text = clean_text(review_text)
+        text_vectorized = vectorizer.transform([cleaned_text])
+        prediction = model.predict(text_vectorized)[0]
+        if prediction == 1:
+            return "✅ Positive Review"
+        else:
+            return "❌ Negative Review"
     except Exception as e:
-        st.error(f"Error loading model: {e}")
-        st.stop()
+        return f"Error during prediction: {e}"
 
-def main():
-    # Set page configuration
-    st.set_page_config(
-        page_title="IMDB Sentiment Analysis",
-        page_icon="🎬",
-        layout="centered"
-    )
-    
-    # App title
-    st.title("🎬 IMDB Sentiment Analysis")
-    st.markdown("Enter a movie review below to analyze its sentiment!")
-    
-    # Load model and vectorizer
-    model, vectorizer = load_model_and_vectorizer()
-    
-    # Text input area
-    review_text = st.text_area(
-        "Movie Review:",
-        placeholder="Enter your movie review here...",
-        height=150
-    )
-    
-    # Analyze button
-    if st.button("Analyze", type="primary"):
-        if not review_text.strip():
-            st.warning("Please enter a review first.")
-        else:
-            try:
-                # Clean the input text
-                cleaned_text = clean_text(review_text)
-                
-                # Transform text using the loaded vectorizer
-                text_vectorized = vectorizer.transform([cleaned_text])
-                
-                # Make prediction
-                prediction = model.predict(text_vectorized)[0]
-                
-                # Display result
-                if prediction == 1:
-                    st.success("✅ Positive Review")
-                else:
-                    st.error("❌ Negative Review")
-                    
-            except Exception as e:
-                st.error(f"Error during prediction: {e}")
-    
-    # Add some info about the model
-    with st.expander("ℹ️ About this model"):
-        st.write("""
-        This sentiment analysis model was trained on the IMDB movie reviews dataset 
-        using Logistic Regression with TF-IDF features. The model classifies movie 
-        reviews as either positive or negative sentiment.
-        """)
+# Create Gradio interface
+iface = gr.Interface(
+    fn=analyze_review,
+    inputs=gr.Textbox(lines=6, placeholder="Enter your movie review here..."),
+    outputs="text",
+    title="🎬 IMDB Sentiment Analysis",
+    description="Enter a movie review and get a Positive/Negative sentiment prediction."
+)
 
 if __name__ == "__main__":
-    main()
+    iface.launch()