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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()
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