Adding all files

Dockerfile

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+# Use a lightweight Python base image
+FROM python:3.10-slim
+
+# Set working directory
+WORKDIR /app
+
+# Install system dependencies (if needed for TensorFlow)
+RUN apt-get update && apt-get install -y \
+    libgl1-mesa-glx \
+    libglib2.0-0 \
+    && rm -rf /var/lib/apt/lists/*
+
+# Copy requirements file and install dependencies
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy all application files
+COPY . .
+
+# Expose the port Hugging Face Spaces expects
+EXPOSE 7860
+
+# Run the FastAPI app with Uvicorn
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

IAPLD .h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:93166046ba63f4305eef4e3a3c28604a477c1e101cd8ea346b3661614afe96ae
+size 106687904

app.py

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+from fastapi import FastAPI, File, UploadFile, HTTPException
+from tensorflow.keras.models import load_model
+import numpy as np
+from PIL import Image
+import io
+from fastapi.middleware.cors import CORSMiddleware
+
+# Initialize FastAPI app
+app = FastAPI(title="Image Classification API")
+
+# Add CORS middleware
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Load the Keras model once at startup
+try:
+    model = load_model('IAPLD.h5')
+except Exception as e:
+    raise RuntimeError(f"Failed to load model 'IAPLD.h5': {str(e)}")
+
+# Define class names (adjust if model outputs 3 classes instead of 4)
+CLASS_NAMES = ['Potato___healthy', 'Potato___Early_blight','Potato___Late_blight']
+
+# Function to preprocess the uploaded image
+def preprocess_image(image: Image.Image) -> np.ndarray:
+    
+    # Resize to match model input shape (250, 250 as per your code)
+    image = image.resize((250, 250))  # Adjust to (256, 256) if model expects that
+    
+    # Convert to NumPy array and normalize to 0-1 range
+    image_array = np.array(image) / 255.0
+    
+    # Add batch dimension (1, 250, 250, 3)
+    image_array = np.expand_dims(image_array, axis=0)
+    
+    return image_array
+
+# Root endpoint
+@app.get("/")
+async def root():
+    return {"message": "Welcome to the Image Classification API. Use POST /predict/ to upload an image."}
+
+# Prediction endpoint
+@app.post("/predict/")
+async def predict(file: UploadFile = File(...)):
+    if not file.content_type.startswith('image/'):
+        raise HTTPException(status_code=400, detail="Uploaded file must be an image")
+
+    try:
+        # Read the image bytes
+        contents = await file.read()
+        
+        # Open as PIL image
+        image = Image.open(io.BytesIO(contents))
+        print("Image size:", image.size)  # Debug: Check image size
+        # Preprocess the image
+        image_array = preprocess_image(image)
+        print("Image shape:", image_array.shape)  # Debug: Check input shape
+        
+        # Make prediction (model outputs probabilities directly)
+        predictions = model.predict(image_array)
+        print("Probabilities:", predictions)  # Debug: Direct probabilities
+        
+        # Get predicted class and confidence
+        class_index = np.argmax(predictions[0])
+        class_name = CLASS_NAMES[class_index]
+        probability = float(predictions[0][class_index])
+        
+        # Return prediction result
+        return {
+            "predicted_class": class_name,
+            "confidence": probability
+        }
+    
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Error processing image: {str(e)}")
+
+# Run the app with: uvicorn main:app --reload
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)

requirements.txt

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+fastapi==0.115.4
+uvicorn==0.32.0
+tensorflow==2.15.0
+numpy==1.26.4
+pillow==10.2.0