Upload app.py

app.py

@@ -1,5 +1,34 @@
+from huggingface_hub import hf_hub_download
 import joblib
-knn = joblib.load('knn_model.pkl')
-import warnings
-warnings.filterwarnings('ignore')
-print(knn.predict([[0.2,0.03,0.0,1.0,0.0]]))
+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.pkl")
+
+# Load the model
+knn = joblib.load(model_path)
+
+# Define the prediction function
+def predict(input_data):
+    # Convert input_data to numpy array
+    input_data = np.array(input_data).reshape(1, -1)
+    # Make predictions
+    predictions = knn.predict([[0.2,0.03,0.0,1.0,0.0]])
+    return predictions[0]
+
+# Create Gradio interface
+# Adjust the input components based on the number of features your model expects
+input_components = [gr.inputs.Number(label=f"Feature {i+1}") for i in range(4)]
+output_component = gr.outputs.Textbox(label="Prediction")
+
+iface = gr.Interface(
+    fn=predict,
+    inputs=input_components,
+    outputs=output_component,
+    title="KNN Model Prediction",
+    description="Enter values for each feature to get a prediction."
+)
+
+# Launch the interface
+iface.launch()