main app

app.py

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+import gradio as gr
+import joblib  # Only for demonstration, use joblib if your model is saved with joblib
+
+# Load your machine learning model
+# Replace this with your actual model loading code
+# Example with a dummy sentiment analysis model
+# model = joblib.load('your_model_path.joblib')
+def predict_sentiment(text):
+    # Replace this with your actual model prediction code
+    # Example with a dummy sentiment analysis model
+    model = joblib.load("pipeline_model.joblib")
+    prediction = model.predict([text])
+    return prediction
+
+# Create a Gradio interface
+iface = gr.Interface(
+    fn=predict_sentiment,  # Your prediction function
+    inputs=gr.Textbox(),   # Text input box
+    outputs=gr.Textbox(),  # Display prediction output
+    live=True               # Update predictions as you type
+)
+
+# Launch the interface on a local server
+iface.launch()