Update app.py

app.py

@@ -2,68 +2,64 @@ 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)
+# Load all models and the vectorizer
+with open("random_forest_model.pkl", "rb") as f:
+    random_forest_model = pickle.load(f)
 
-with open('logistic_model.pkl', 'rb') as file:
-    logistic_model = pickle.load(file)
+with open("logistic_model.pkl", "rb") as f:
+    logistic_model = pickle.load(f)
 
-with open('knn_yelp_model.pkl', 'rb') as file:
-    knn_yelp_model = pickle.load(file)
+with open("knn_yelp_model.pkl", "rb") as f:
+    knn_yelp_model = pickle.load(f)
 
-with open('svm_linear.pkl', 'rb') as file:
-    svm_linear = pickle.load(file)
+with open("svm_linear.pkl", "rb") as f:
+    svm_linear = pickle.load(f)
 
-with open('svm_poly.pkl', 'rb') as file:
-    svm_poly = pickle.load(file)
+with open("svm_poly.pkl", "rb") as f:
+    svm_poly = pickle.load(f)
 
-with open('svm_rbf.pkl', 'rb') as file:
-    svm_rbf = pickle.load(file)
+with open("svm_rbf.pkl", "rb") as f:
+    svm_rbf = pickle.load(f)
 
-# 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
-}
+with open("vectorizer.pkl", "rb") as f:
+    vectorizer = pickle.load(f)
 
 # 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])
+def predict_sentiment(review, model_name):
+    # Vectorize the input review
+    review_vec = vectorizer.transform([review])
     
     # Select the model based on user input
-    model = models.get(model_name)
+    if model_name == 'Random Forest':
+        model = random_forest_model
+    elif model_name == 'Logistic Regression':
+        model = logistic_model
+    elif model_name == 'KNN':
+        model = knn_yelp_model
+    elif model_name == 'SVM Linear':
+        model = svm_linear
+    elif model_name == 'SVM Poly':
+        model = svm_poly
+    elif model_name == 'SVM RBF':
+        model = svm_rbf
     
-    if model:
-        # Predict class probabilities
-        prob = model.predict_proba(review_vector)
-        return prob
-    else:
-        return "Model not found."
+    # Predict probabilities for each class (assuming binary classification)
+    probabilities = model.predict_proba(review_vec)[0]
+    return {"Negative": probabilities[0], "Positive": probabilities[1]}
 
-# Gradio Interface
-interface = gr.Interface(
-    fn=predict_class_probabilities, 
+# Set up the Gradio interface
+iface = gr.Interface(
+    fn=predict_sentiment, 
     inputs=[
-        gr.Textbox(label="Enter your review", placeholder="Type a review here..."),
+        gr.Textbox(label="Enter Your Review"),
         gr.Dropdown(
-            label="Select a Model", 
-            choices=["Random Forest", "Logistic Regression", "KNN", "SVM Linear", "SVM Polynomial", "SVM RBF"]
-        )
+            choices=["Random Forest", "Logistic Regression", "KNN", "SVM Linear", "SVM Poly", "SVM RBF"],
+            label="Select Model"
+        ),
     ], 
-    outputs="json", 
+    outputs=gr.Label(num_top_classes=2), 
     live=True
 )
 
 # Launch the interface
-interface.launch()
+iface.launch()