app.py

from huggingface_hub import hf_hub_download
import pickle
import gradio as gr
import numpy as np

# Download the model from Hugging Face Hub
model_path = hf_hub_download(repo_id="suryadev1/knn", filename="knn_model_pc.pkl")

# Load the model
with open(model_path, 'rb') as f:
    knn = pickle.load(f)

# Define the prediction function
def predict(input_data):
    # Convert input_data to numpy array
    input=input_data.split(' ')
    first=float(input[0])
    second=float(input[1])
    third=float(input[2])
    fourth=float(input[3])
    fifth=float(input[4])
    # Make predictions
    predictions = knn.predict([[first,second,third,fourth,fifth]])
    return predictions[0]



iface = gr.Interface(
    fn=predict,
    inputs='text',
    outputs='text',
    title="KNN Model Prediction",
    description="Enter values for each feature with spaces to get a prediction."
)

# Launch the interface
iface.launch()