created It20109776 back end

api/disease.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()
+
+MODEL = tf.keras.models.load_model("../saved_models/Version34")
+
+# Modify the CLASS_NAMES list to include additional details
+CLASS_NAMES = [
+    "Brown Spot |  Fungus Sphaerulina oryzina   |  Iprodione, Propiconazole, Azoxystrobin, Trifloxystrobin, Carbendazim ",
+    "No Disease Detected - Healthy | Everything looks fine | Not required",
+    "Hispa | Armigera feed externally on leaf tissue | Carbofuran 3% CG 10 kg; emamectin benzoate 1.9 EC 170 ml; malathion 5 DP 10 kg; malathion 50 EC 460 ml.",
+    "Leaf Blast| Fungus Magnaporthe oryzae. | Spray Carbendazim or Edifenphos @ 1.0 gm/lit , Pre-Tillering to Mid-Tillering: Light at 2 to 5 % disease severities - Apply Edifenphos or Carbendazim @ 1.0 gm/lit."
+]
+
+def read_file_as_image(data) -> np.ndarray:
+    image = np.array(Image.open(BytesIO(data)))
+    return image
+
+@app.post("/predictLeaf")
+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("/")
+async def get_index():
+    with open('diseases.html', 'r') as f:
+        return HTMLResponse(content=f.read(), status_code=200)
+
+if __name__ == "__main__":
+    uvicorn.run(app, host='localhost', port=8000)