app.py

pip install gradio fastapi uvicorn

import gradio as gr
import pandas as pd
from huggingface_hub import hf_hub_download
import joblib
from fastapi import FastAPI
from gradio.routes import Route

# Load the model
repo_id = "rmaitest/mlmodel2"
model_file = "house_price_model.pkl"  # Adjust as necessary

# Download and load the model
model_path = hf_hub_download(repo_id, model_file)
model = joblib.load(model_path)

# Define the prediction function
def predict_price(size, bedrooms, age):
    # Create a DataFrame from the input
    input_data = pd.DataFrame({
        'Size (sq ft)': [size],
        'Number of Bedrooms': [bedrooms],
        'Age of House (years)': [age]
    })
    
    # Make prediction
    prediction = model.predict(input_data)
    return prediction[0]

# Define the Gradio interface
iface = gr.Interface(
    fn=predict_price,
    inputs=[
        gr.Number(label="Size (sq ft)"),
        gr.Number(label="Number of Bedrooms"),
        gr.Number(label="Age of House (years)")
    ],
    outputs=gr.Number(label="Predicted Price ($)"),
    title="House Price Prediction",
    description="Enter the size, number of bedrooms, and age of the house to get the predicted price."
)

# Create FastAPI app
app = FastAPI()

# Create a route for the /predict API endpoint
@app.post("/predict")
async def predict(size: float, bedrooms: int, age: int):
    # Call the Gradio function manually for the API route
    return iface.fn(size, bedrooms, age)

# Launch Gradio interface (only for UI purposes, if needed)
if __name__ == "__main__":
    iface.launch(share=True)