Add application file

Dockerfile

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+# Read the doc: https://huggingface.co/docs/hub/spaces-sdks-docker
+# you will also find guides on how best to write your Dockerfile
+
+FROM python:3.9
+
+RUN useradd -m -u 1000 user
+USER user
+ENV PATH="/home/user/.local/bin:$PATH"
+
+WORKDIR /app
+
+COPY --chown=user ./requirements.txt requirements.txt
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+COPY --chown=user . /app
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+from fastapi import FastAPI, File, UploadFile, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+import io
+import uvicorn
+import tempfile
+import cv2
+
+# Initialize FastAPI app
+app = FastAPI(title="Plant Disease Detection API", version="1.0.0")
+
+# Add CORS middleware to allow requests from your frontend
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # In production, replace with your frontend URL
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Load your model
+model = tf.keras.models.load_model('trained_modela.keras')
+
+# Define your class names (update with your actual classes)
+class_name = ['Apple___Apple_scab',
+    'Apple___Black_rot',
+    'Apple___Cedar_apple_rust',
+    'Apple___healthy',
+    'Blueberry___healthy',
+    'Cherry_(including_sour)___Powdery_mildew',
+    'Cherry_(including_sour)___healthy',
+    'Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot',
+    'Corn_(maize)___Common_rust_',
+    'Corn_(maize)___Northern_Leaf_Blight',
+    'Corn_(maize)___healthy',
+    'Grape___Black_rot',
+    'Grape___Esca_(Black_Measles)',
+    'Grape___Leaf_blight_(Isariopsis_Leaf_Spot)',
+    'Grape___healthy',
+    'Orange___Haunglongbing_(Citrus_greening)',
+    'Peach___Bacterial_spot',
+    'Peach___healthy',
+    'Pepper,_bell___Bacterial_spot',
+    'Pepper,_bell___healthy',
+    'Potato___Early_blight',
+    'Potato___Late_blight',
+    'Potato___healthy',
+    'Raspberry___healthy',
+    'Soybean___healthy',
+    'Squash___Powdery_mildew',
+    'Strawberry___Leaf_scorch',
+    'Strawberry___healthy',
+    'Tomato___Bacterial_spot',
+    'Tomato___Early_blight',
+    'Tomato___Late_blight',
+    'Tomato___Leaf_Mold',
+    'Tomato___Septoria_leaf_spot',
+    'Tomato___Spider_mites Two-spotted_spider_mite',
+    'Tomato___Target_Spot',
+    'Tomato___Tomato_Yellow_Leaf_Curl_Virus',
+    'Tomato___Tomato_mosaic_virus',
+    'Tomato___healthy']
+
+
+@app.get("/")
+async def root():
+    return {"message": "Plant Disease Detection API", "version": "1.0.0"}
+
+@app.post("/predict")
+async def predict_disease(file: UploadFile = File(...)):
+    """
+    Predict plant disease from uploaded image
+    """
+    try:
+        # Validate file type
+        # Validate file type
+        if not file.content_type.startswith('image/'):
+            raise HTTPException(status_code=400, detail="File must be an image")
+
+        # Save uploaded file temporarily
+        with tempfile.NamedTemporaryFile(suffix=".jpg", delete=False) as tmp:
+            temp_path = tmp.name
+            contents = await file.read()
+            tmp.write(contents)
+        
+        # Read image using OpenCV
+        img = cv2.imread(temp_path)
+        if img is None:
+            raise HTTPException(status_code=400, detail="Invalid image file")
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+        
+        image = tf.keras.preprocessing.image.load_img(temp_path,target_size=(128, 128))
+        
+        input_arr = tf.keras.preprocessing.image.img_to_array(image)
+        input_arr = np.array([input_arr])  # Convert single image to batch
+
+        # Predict
+        prediction = model.predict(input_arr)
+        result_index = np.argmax(prediction)
+        confidence = prediction[0][result_index]
+        disease_name = class_name[result_index]
+
+        return {
+            "success": True,
+            "disease": disease_name,
+            "confidence": confidence
+        }
+
+    except HTTPException as he:
+        raise he
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Prediction error: {str(e)}")
+
+@app.get("/health")
+async def health_check():
+    return {"status": "healthy"}
+
+@app.get("/classes")
+async def get_classes():
+    """Get all available disease classes"""
+    return {"classes": class_name}
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=7860)

requirements.txt

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+tensorFlow == 2.19.0
+numpy>=1.26.0,<2.2.0
+gradio == 5.44.1
+pillow == 10.4.0
+requests == 2.32.3
+opencv-python-headless == 4.12.0.88
+fastapi==0.116.1
+uvicorn==0.35.0