Model deployed

Dockerfile

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+# Use an official Python runtime as a parent image
+FROM python:3.9-slim
+
+# Set the working directory in the container
+WORKDIR /app
+
+# Copy the current directory contents into the container
+COPY . /app
+
+# Install the required packages from requirements.txt
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Expose port 8000 for the FastAPI app
+EXPOSE 8000
+
+# Command to run the FastAPI app with Uvicorn
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8000"]

app.py

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+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+import pandas as pd
+import joblib
+
+# Load the trained model
+model = joblib.load("model.pkl")  # Ensure your model is saved as 'model.pkl'
+
+app = FastAPI()
+
+# Add CORS middleware to allow requests from any origin
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # Allow all origins (adjust if needed)
+    allow_credentials=True,
+    allow_methods=["*"],  # Allow all methods (GET, POST, etc.)
+    allow_headers=["*"],  # Allow all headers
+)
+
+@app.post("/predict")
+async def predict(features: dict):
+    # Convert the input into a DataFrame
+    input_data = pd.DataFrame([features])
+    
+    # Make prediction using the trained model
+    prediction = model.predict(input_data)
+    
+    return {"is_potentially_hazardous_asteroid": int(prediction[0])}
+

model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:21c23c7f17c0d72b85d0b416e14f01a70ee56833197740ee5ce168819b15148f
+size 3870

requirements.txt

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+fastapi
+uvicorn
+pandas
+scikit-learn
+joblib

test.py

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+import requests
+
+# URL of the deployed API (replace with your actual space URL)
+url = "https://<your-space-name>.hf.space/predict"
+
+# Sample input data
+data = {
+    "absolute_magnitude_h": 22.1,
+    "estimated_diameter_min_km": 0.127,
+    "estimated_diameter_max_km": 0.285,
+    "relative_velocity_km_per_sec": 5.67,
+    "miss_distance_km": 386000.0
+}
+
+# Make a request to the API
+response = requests.post(url, json=data)
+
+# Display the prediction result
+print(response.json())