Create app.py

app.py

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+import gradio as gr
+import pickle
+import numpy as np
+
+# Load all the models
+with open('random_forest_model.pkl', 'rb') as file:
+    random_forest_model = pickle.load(file)
+
+with open('logistic_model.pkl', 'rb') as file:
+    logistic_model = pickle.load(file)
+
+with open('knn_yelp_model.pkl', 'rb') as file:
+    knn_yelp_model = pickle.load(file)
+
+with open('svm_linear.pkl', 'rb') as file:
+    svm_linear = pickle.load(file)
+
+with open('svm_poly.pkl', 'rb') as file:
+    svm_poly = pickle.load(file)
+
+with open('svm_rbf.pkl', 'rb') as file:
+    svm_rbf = pickle.load(file)
+
+# Store models in a dictionary for easy access
+models = {
+    "Random Forest": random_forest_model,
+    "Logistic Regression": logistic_model,
+    "KNN": knn_yelp_model,
+    "SVM Linear": svm_linear,
+    "SVM Polynomial": svm_poly,
+    "SVM RBF": svm_rbf
+}
+
+# Function to predict class probabilities
+def predict_class_probabilities(review_text, model_name):
+    # Convert review text to the format the model expects (using vectorizer)
+    # Assuming you have a vectorizer stored as 'vectorizer.pkl'
+    with open('vectorizer.pkl', 'rb') as file:
+        vectorizer = pickle.load(file)
+    
+    # Transform the review text into a vector
+    review_vector = vectorizer.transform([review_text])
+    
+    # Select the model based on user input
+    model = models.get(model_name)
+    
+    if model:
+        # Predict class probabilities
+        prob = model.predict_proba(review_vector)
+        return prob
+    else:
+        return "Model not found."
+
+# Gradio Interface
+interface = gr.Interface(
+    fn=predict_class_probabilities, 
+    inputs=[
+        gr.Textbox(label="Enter your review", placeholder="Type a review here..."),
+        gr.Dropdown(
+            label="Select a Model", 
+            choices=["Random Forest", "Logistic Regression", "KNN", "SVM Linear", "SVM Polynomial", "SVM RBF"]
+        )
+    ], 
+    outputs="json", 
+    live=True
+)
+
+# Launch the interface
+interface.launch()