Create app.py

app.py

@@ -0,0 +1,34 @@
+import joblib
+import numpy as np
+from fastapi import FastAPI
+from pydantic import BaseModel
+import uvicorn
+
+# Initialize FastAPI app
+app = FastAPI()
+
+# Load model and scaler
+model = joblib.load('conme.pkl')
+scaler = joblib.load('scaler.joblib')
+
+class InputData(BaseModel):
+    soil_moisture: float
+    temperature: float
+    air_humidity: float
+    light_intensity: float
+
+@app.post("/predict")
+def predict(data: InputData):
+    # Prepare input data
+    input_data = np.array([[data.soil_moisture, data.temperature, data.air_humidity, data.light_intensity]])
+    
+    # Scale the data
+    std_data = scaler.transform(input_data)
+    
+    # Make prediction
+    prediction = model.predict(std_data)
+    
+    return {"prediction": int(prediction[0])}  # Convert prediction to int (0 or 1)
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=8000)