updated app.py

app.py

@@ -0,0 +1,41 @@
+import gradio as gr
+import joblib
+
+# Load models
+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"),
+}
+
+# Define prediction function
+def predict(review, model_name):
+    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
+if __name__ == "__main__":
+    interface.launch()