Frist Test

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:3.10
+
+WORKDIR /code
+
+COPY ./requirements.txt /code/requirements.txt
+RUN pip install --upgrade pip
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+COPY . .
+
+CMD ["gunicorn","-b","0.0.0.0:7860","--timeout","600" ,"app:app"]

app.py

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+from fastapi import FastAPI, UploadFile, File, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from keras.models import load_model
+import numpy as np
+from PIL import Image
+import io
+
+app = FastAPI()
+
+# Load the Keras model
+model = load_model('keras_model.h5')  # Replace 'your_model.h5' with the path to your .h5 file
+
+# Function to preprocess the input image
+def preprocess_image(img):
+    img = img.resize((224, 224))  # Assuming input size of 224x224
+    img_array = np.array(img)
+    img_array = img_array.astype('float32') / 255  # Normalization
+    img_array = np.expand_dims(img_array, axis=0)
+    return img_array
+
+# Define a function to predict the class of an image
+def predict_class(img):
+    processed_image = preprocess_image(img)
+    prediction = model.predict(processed_image)
+    return prediction
+
+@app.post("/predict/")
+async def predict(file: UploadFile = File(...)):
+    contents = await file.read()
+    img = Image.open(io.BytesIO(contents))
+    prediction = predict_class(img)
+    
+    # Assuming your model output is a list of probabilities for each class
+    # You may need to modify this based on your model's output
+    prediction = prediction.tolist()[0]
+    
+    # Assuming you have two classes: Blight disease and Powdery mildew
+    # Modify this based on your actual class names
+    class_names = ["Blight disease on grape leaves", "Powdery mildew on grapes"]
+    result = {"prediction": class_names[np.argmax(prediction)], "probabilities": prediction}
+    return result
+
+# Allow CORS (Cross-Origin Resource Sharing) for all origins
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["GET", "POST", "OPTIONS"],
+    allow_headers=["*"],
+)
+
+# Handle OPTIONS requests
+@app.options("/predict/")
+async def options_predict():
+    return {"methods": ["POST"]}

d.sh

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+git add .
+git commit -m "Frist Test"
+git push

keras_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:92e8795ce70d47fbc85f9b578a54246ec53f22f2471c2ee6d9c31d79a4400f03
+size 2453432

labels.txt

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+0 Blight disease on grape leaves
+1 Powdery mildew on grapes

requirements.txt

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+flask
+flask_cors
+gunicorn
+uvicorn
+keras
+tensorflow
+numpy
+pillow