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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()
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