Update app.py

app.py

@@ -1,34 +1,32 @@
+import gradio as gr
 import joblib
 import numpy as np
-from fastapi import FastAPI
-from pydantic import BaseModel
-import uvicorn
 
-# Initialize FastAPI app
-app = FastAPI()
+# Load the model and scaler
+model = joblib.load("conme.pkl")  # Đảm bảo tên tệp phù hợp
+scaler = joblib.load("scaler.joblib")
 
-# 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):
+def predict(soil_moisture, temperature, air_humidity, light_intensity):
     # Prepare input data
-    input_data = np.array([[data.soil_moisture, data.temperature, data.air_humidity, data.light_intensity]])
-    
+    input_data = np.array([[soil_moisture, temperature, air_humidity, 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)
+    return int(prediction[0])  # Convert prediction to int (0 or 1)
+
+# Set up Gradio interface
+iface = gr.Interface(
+    fn=predict,
+    inputs=[
+        gr.inputs.Number(label="Soil Moisture"),
+        gr.inputs.Number(label="Temperature"),
+        gr.inputs.Number(label="Air Humidity"),
+        gr.inputs.Number(label="Light Intensity"),
+    ],
+    outputs="text",
+    title="Plant Watering Prediction"
+)
 
 if __name__ == "__main__":
-    uvicorn.run(app, host="0.0.0.0", port=8000)
+    iface.launch()