Create app.py

app.py

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+import gradio as gr
+import pickle
+import pandas as pd
+from huggingface_hub import hf_hub_download
+
+# Load your model from your existing model repository
+repo_id = "YOUR_USERNAME/random-forest-model"  # CHANGE THIS
+model_path = hf_hub_download(repo_id=repo_id, filename="random_forest_model.pkl")
+
+with open(model_path, 'rb') as f:
+    model = pickle.load(f)
+
+# Prediction function - adjust feature names as needed
+def predict(feature1, feature2, feature3):
+    df = pd.DataFrame([[feature1, feature2, feature3]], 
+                     columns=['feature1', 'feature2', 'feature3'])
+    pred = model.predict(df)[0]
+    prob = model.predict_proba(df)[0].max()
+    return f"Prediction: {pred} (Confidence: {prob:.2f})"
+
+# Create the API endpoint
+demo = gr.Interface(
+    fn=predict,
+    inputs=[
+        gr.Number(label="Feature 1"),
+        gr.Number(label="Feature 2"), 
+        gr.Number(label="Feature 3")
+    ],
+    outputs=gr.Textbox(label="Result"),
+    title="Random Forest API"
+)
+
+demo.launch()