created It20236946 backend

api/harvest.py

+from fastapi import FastAPI, File, UploadFile
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import FileResponse, HTMLResponse
+import uvicorn
+import numpy as np
+from io import BytesIO
+from PIL import Image
+import tensorflow as tf
+
+app = FastAPI()
+
+# CORS middleware for handling cross-origin requests
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+MODEL = tf.keras.models.load_model("../Models_main/version11")
+
+CLASS_NAMES = ["Flowering Stage", "Maturity Stage", "Out of Domain", "Re-productive Stage", "Ripening Stage"]
+
+
+def read_file_as_image(data) -> np.ndarray:
+    image = np.array(Image.open(BytesIO(data)))
+    return image
+
+@app.post("/predict")
+async def predict(file: UploadFile = File(...)):
+    image = read_file_as_image(await file.read())
+    img_batch = np.expand_dims(image, 0)
+    predictions = MODEL.predict(img_batch)
+    predicted_class = CLASS_NAMES[np.argmax(predictions[0])]
+    confidence = np.max(predictions[0])
+    return {
+        'class': predicted_class,
+        'confidence': float(confidence)
+    }
+
+@app.get("/", response_class=HTMLResponse)
+async def get_index():
+    with open('paddyStage.html', 'r') as f:
+        return f.read()
+
+if __name__ == "__main__":
+    uvicorn.run(app, host='localhost', port=8010)