Deploy DenseNet121 prediction API with LFS

DenseNet121.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:076f897b3e5fc10e70b2bafb65fece9eae96fe0eeac068fedaf64cf33aa938fd
+size 29509592

Dockerfile

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+# Read the doc: https://huggingface.co/docs/hub/spaces-sdks-docker
+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 all files including the model DenseNet121.h5
+COPY --chown=user . /app
+
+# Hugging Face Spaces expect port 7860
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+import io
+import numpy as np
+import tensorflow as tf
+from fastapi import FastAPI, File, UploadFile, HTTPException
+from PIL import Image
+from typing import Dict
+
+app = FastAPI(title="DenseNet121 Image Prediction API")
+
+# Labels dictionary as per requirements
+LABELS = {
+    0: {"short": "DR", "full": "Diabetic Retinopathy"},
+    1: {"short": "MH", "full": "Media Haze"},
+    2: {"short": "NL", "full": "Normal"}
+}
+
+# Global variable to store the model
+model = None
+
+@app.on_event("startup")
+async def load_model():
+    global model
+    try:
+        # Loading the fully trained model
+        model = tf.keras.models.load_model("DenseNet121.h5")
+        print("Model loaded successfully.")
+    except Exception as e:
+        print(f"Error loading model: {e}")
+
+def preprocess_image(image: Image.Image) -> np.ndarray:
+    """
+    Preprocess the image according to requirements:
+    - RGB format
+    - Resize to 224x224
+    - Normalize by dividing by 255
+    """
+    if image.mode != "RGB":
+        image = image.convert("RGB")
+    
+    image = image.resize((224, 224))
+    img_array = np.array(image)
+    
+    # Normalize by 255
+    img_array = img_array.astype(np.float32) / 255.0
+    
+    # Add batch dimension
+    img_array = np.expand_dims(img_array, axis=0)
+    
+    return img_array
+
+@app.get("/")
+async def root():
+    return {"message": "DenseNet121 Prediction API is running", "model_status": "Loaded" if model else "Not Loaded"}
+
+@app.post("/predict")
+async def predict(file: UploadFile = File(...)):
+    """
+    Endpoint for image prediction.
+    Accepts an image file and returns softmax probabilities and class labels.
+    """
+    if model is None:
+        raise HTTPException(status_code=500, detail="Model not loaded.")
+
+    try:
+        # Read image
+        contents = await file.read()
+        image = Image.open(io.BytesIO(contents))
+        
+        # Preprocess
+        processed_image = preprocess_image(image)
+        
+        # Predict
+        predictions = model.predict(processed_image)[0] # Softmax probabilities
+        
+        # Prepare response
+        results = {}
+        summary_parts = []
+        
+        for i, prob in enumerate(predictions):
+            label_info = LABELS[i]
+            percentage = prob * 100
+            results[label_info["full"]] = f"{percentage:.2f}%"
+            summary_parts.append(f"{int(round(percentage))}% {label_info['short']}")
+        
+        summary_text = ", ".join(summary_parts)
+        
+        return {
+            "prediction_summary": summary_text,
+            "detailed_probabilities": results,
+            "top_prediction": LABELS[np.argmax(predictions)]["full"]
+        }
+
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Prediction error: {str(e)}")

requirements.txt

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+fastapi
+uvicorn[standard]
+tensorflow
+Pillow
+python-multipart
+numpy