Update app.py

app.py

@@ -1,30 +1,39 @@
 import gradio as gr
-import joblib
 import numpy as np
-from sklearn.feature_extraction.text import CountVectorizer
-from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score
-from sklearn.model_selection import train_test_split
-from sklearn.svm import SVC
-from sklearn.neighbors import KNeighborsClassifier
-from sklearn.linear_model import LogisticRegression
-from sklearn.ensemble import RandomForestClassifier
+from joblib import load
 
-# Load your pre-trained models (make sure the models are uploaded to the Hugging Face Space)
-logreg_model = joblib.load('best_lr_model.pkl')
-knn_model = joblib.load('best_knn_model.pkl')
-svc_model = joblib.load('best_svc_model.pkl')
-rf_model = joblib.load('best_rf_model.pkl')
+# Step 6.1: Load your trained models 
+logreg_model = load('best_lr_model.pkl')  
+knn_model = load('best_knn_model.pkl')  
+svc_model = load('best_svc_model.pkl')  
+rf_model = load('best_rf_model.pkl')  
 
-# Load the vectorizer (assuming the same vectorizer used during training)
-vectorizer = joblib.load('vectorizer.pkl')  # You must upload this file as well
+# Step 6.2: Define different input components
 
-# Prediction function
+# a. define textbox input for review comment (as per your example)
+review_input = gr.Textbox(label="Review Comment", lines=2, placeholder="Enter your review comment here...")
+
+# b. define dropdown input for model selection (mimicking the dropdown for models like NaiveBayes, Logistic Regression, etc.)
+model_dropdown = gr.Dropdown(
+    choices=["Logistic Regression", "K-Nearest Neighbors", "Support Vector Machine", "Random Forest"], 
+    label="Select Model"
+)
+
+# Step 6.3: Define output component for predicted sentiment class and probability
+output_sentiment = gr.Textbox(label="Predicted Sentiment Class", placeholder="Predicted sentiment will appear here...")
+output_probability = gr.Textbox(label="Predicted Probability", placeholder="Probabilities will appear here...")
+
+# Step 6.4: Define a function to predict sentiment using the selected model
 def predict_sentiment(review_text, model_name):
-    # Preprocess the review text with the same vectorizer
-    review_features = vectorizer.transform([review_text])  # Transform review to features
-    review_features = review_features.toarray()  # Ensure it's in array form
+    # Preprocess the review_text using vectorizer (replace with your preprocessing logic)
+    # For example, use TfidfVectorizer or CountVectorizer as needed
+    # Here I'm assuming you've preprocessed the input text in the same way you did during training
 
-    # Select the model based on the input
+    # Dummy text transformation for demonstration purposes
+    # Replace with actual vectorizer code (e.g., vectorizer.transform([review_text])) if needed
+    transformed_text = np.array([review_text])  # Example transformation
+
+    # Choose the model based on the selected dropdown
     if model_name == "Logistic Regression":
         model = logreg_model
     elif model_name == "K-Nearest Neighbors":
@@ -33,40 +42,20 @@ def predict_sentiment(review_text, model_name):
         model = svc_model
     elif model_name == "Random Forest":
         model = rf_model
-    
-    # Use the model to predict sentiment probabilities
-    prob = model.predict_proba(review_features)  # Use predict_proba to get probabilities
-    
-    # Return positive and negative sentiment probabilities
-    return {"Positive Comment": prob[0][1], "Negative Comment": prob[0][0]}
-
-# Gradio Interface setup
-def create_interface():
-    # Create a dropdown for model selection
-    model_dropdown = gr.Dropdown(
-        choices=["Logistic Regression", "K-Nearest Neighbors", "Support Vector Machine", "Random Forest"],
-        label="Select Model"
-    )
-
-    # Create a textbox for the review input
-    review_input = gr.Textbox(
-        lines=2, placeholder="Enter your review comment here...", label="Review Comment"
-    )
-
-    # Create a JSON output to display predicted sentiment and probabilities
-    output = gr.JSON()
-
-    # Create the Gradio interface
-    interface = gr.Interface(
-        fn=predict_sentiment, 
-        inputs=[review_input, model_dropdown], 
-        outputs=output, 
-        live=True,  # Optionally update in real-time
-        description="Sentiment Analysis Model",
-    )
-
-    return interface
 
-# Launch the Gradio interface
-if __name__ == "__main__":
-    create_interface().launch()
+    # Make prediction using the model
+    prob = model.predict_proba(transformed_text)  # Assuming models use `predict_proba`
+    sentiment = model.predict(transformed_text)[0]  # Get the predicted class
+
+    # Return the predicted sentiment and the probabilities
+    return sentiment, f"Positive: {prob[0][1]*100:.2f}%, Negative: {prob[0][0]*100:.2f}%"
+
+# Step 6.5: Put all components together in Gradio's interface
+gr.Interface(
+    fn=predict_sentiment,
+    inputs=[review_input, model_dropdown],
+    outputs=[output_sentiment, output_probability],
+    live=True,  # Enable live updates as the user types
+    description="Sentiment Analysis Model",
+    theme="compact"  # Use a more compact layout for the interface
+).launch()