Upload app.py

app.py

@@ -5,37 +5,58 @@ import joblib
 models = {
     "Logistic Regression": joblib.load("models/best_model.joblib"),
     "Random Forest": joblib.load("models/random_forest_model.joblib"),
-    "SVM (Linear)": joblib.load("models/svm_model_linear.joblib"),
-    "SVM (Polynomial)": joblib.load("models/svm_model_polynomial.joblib"),
-    "SVM (RBF)": joblib.load("models/svm_model_rbf.joblib"),
     "KNN": joblib.load("models/trained_knn_model.joblib"),
 }
 
+# Load vectorizer
+vectorizer = joblib.load("models/vectorizer.joblib")
+
 # Define prediction function
-def predict(review, model_name):
+def predict_sentiment(review, model_name):
+    # Transform the review text using the vectorizer
+    processed_review = vectorizer.transform([review])
+
+    # Select the model
     model = models[model_name]
-    prediction = model.predict([review])[0]
-    probabilities = model.predict_proba([review])[0]
-    return {
-        "Predicted Class": str(prediction),
-        "Class Probabilities": {
-            "Class 0": probabilities[0],
-            "Class 1": probabilities[1],
-        },
-    }
-
-# Create Gradio interface
-interface = gr.Interface(
-    fn=predict,
-    inputs=[
-        gr.Textbox(label="Review Comment"),
-        gr.Dropdown(choices=list(models.keys()), label="Model"),
-    ],
-    outputs=gr.JSON(label="Prediction Results"),
-    title="Text Classification Models",
-    description="Choose a model and provide a review to see the predicted sentiment class.",
-)
-
-# Launch the Gradio app
+
+    # Make predictions
+    predicted_class = model.predict(processed_review)[0]
+    probabilities = model.predict_proba(processed_review)[0]
+
+    # Define sentiment labels
+    sentiment_labels = ["Negative Comment", "Positive Comment"]
+    predicted_label = sentiment_labels[predicted_class]
+
+    # Return probabilities as percentages
+    positive_percentage = probabilities[1] * 100
+    negative_percentage = probabilities[0] * 100
+
+    return predicted_label, positive_percentage, negative_percentage
+
+# Build Gradio interface
+with gr.Blocks() as interface:
+    gr.Markdown("<h1>Text Classification Models</h1>")
+    gr.Markdown("Choose a model and provide a review to see the sentiment analysis results with probabilities displayed as scales.")
+
+    with gr.Row():
+        with gr.Column():
+            review_input = gr.Textbox(label="Review Comment", placeholder="Type your comment here...")
+            model_selector = gr.Dropdown(
+                choices=list(models.keys()), label="Select Model", value="Logistic Regression"
+            )
+            submit_button = gr.Button("Submit")
+
+        with gr.Column():
+            sentiment_output = gr.Textbox(label="Predicted Sentiment Class", interactive=False)
+            positive_progress = gr.Slider(label="Positive Comment Percentage", minimum=0, maximum=100, interactive=False)
+            negative_progress = gr.Slider(label="Negative Comment Percentage", minimum=0, maximum=100, interactive=False)
+
+    submit_button.click(
+        predict_sentiment,
+        inputs=[review_input, model_selector],
+        outputs=[sentiment_output, positive_progress, negative_progress],
+    )
+
+# Launch the app
 if __name__ == "__main__":
     interface.launch()