Hf Commit

.gitignore

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+e/

Dockerfile

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+FROM python:3.9
+
+WORKDIR /code
+
+COPY ./requirements.txt /code/requirements.txt
+
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+COPY . .
+
+CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "7860"]

efficientnetb3-Plant Village Disease-99.71.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:25d6e7bd3d261061d40f574c9d15bce41a83345764f1bfae01fa8bc1bb14eadd
+size 135055192

main.py

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+import os
+import io
+import uvicorn
+import tensorflow as tf
+import numpy as np
+from fastapi import FastAPI, File, UploadFile
+from starlette.middleware.cors import CORSMiddleware
+from tensorflow.keras.models import load_model
+from PIL import Image
+from tensorflow.keras.applications.vgg16 import preprocess_input
+
+# Initialize FastAPI app
+app = FastAPI()
+
+# Add CORS middleware to allow cross-origin requests
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # Adjust this to limit which domains can make requests
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+# Check if GPU is available and use it for TensorFlow operations
+gpus = tf.config.experimental.list_physical_devices('GPU')
+if gpus:
+    try:
+        # Currently, memory growth needs to be the same across GPUs
+        for gpu in gpus:
+            tf.config.experimental.set_memory_growth(gpu, True)
+        logical_gpus = tf.config.experimental.list_logical_devices('GPU')
+        print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
+    except RuntimeError as e:
+        # Memory growth must be set before GPUs have been initialized
+        print(e)
+
+# Load the pre-trained model during app startup
+model = load_model('efficientnetb3-Plant Village Disease-99.71.h5')  # Replace with your actual model path
+
+# Define class labels
+class_labels = {
+    0: 'Apple___Apple_scab',
+    1: 'Apple___Black_rot',
+    2: 'Apple___Cedar_apple_rust',
+    3: 'Apple___healthy',
+    4: 'Blueberry___healthy',
+    5: 'Cherry_(including_sour)___Powdery_mildew',
+    6: 'Cherry_(including_sour)___healthy',
+    7: 'Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot',
+    8: 'Corn_(maize)___Common_rust_',
+    9: 'Corn_(maize)___Northern_Leaf_Blight',
+    10: 'Corn_(maize)___healthy',
+    11: 'Grape___Black_rot',
+    12: 'Grape___Esca_(Black_Measles)',
+    13: 'Grape___Leaf_blight_(Isariopsis_Leaf_Spot)',
+    14: 'Grape___healthy',
+    15: 'Orange___Haunglongbing_(Citrus_greening)',
+    16: 'Peach___Bacterial_spot',
+    17: 'Peach___healthy',
+    18: 'Pepper,_bell___Bacterial_spot',
+    19: 'Pepper,_bell___healthy',
+    20: 'Potato___Early_blight',
+    21: 'Potato___Late_blight',
+    22: 'Potato___healthy',
+    23: 'Raspberry___healthy',
+    24: 'Soybean___healthy',
+    25: 'Squash___Powdery_mildew',
+    26: 'Strawberry___Leaf_scorch',
+    27: 'Strawberry___healthy',
+    28: 'Tomato___Bacterial_spot',
+    29: 'Tomato___Early_blight',
+    30: 'Tomato___Late_blight',
+    31: 'Tomato___Leaf_Mold',
+    32: 'Tomato___Septoria_leaf_spot',
+    33: 'Tomato___Spider_mites Two-spotted_spider_mite',
+    34: 'Tomato___Target_Spot',
+    35: 'Tomato___Tomato_Yellow_Leaf_Curl_Virus',
+    36: 'Tomato___Tomato_mosaic_virus',
+    37: 'Tomato___healthy'
+}
+
+
+# Define a route to accept single image uploads and make predictions
+@app.post("/predict/")
+async def predict_single_image(file: UploadFile):
+    try:
+        # Read the uploaded image
+        image = await file.read()
+        img = Image.open(io.BytesIO(image))
+
+        # Model Params
+        img_size = (224, 224)
+        channels = 3  # either BGR or Grayscale
+        color = 'rgb'
+        img_shape = (img_size[0], img_size[1], channels)
+
+        # Preprocess the image to match the input requirements of your model
+        img = img.resize((224, 224))  # Adjust the size as needed
+        img = np.asarray(img)
+        img = preprocess_input(img)
+
+        # Make a prediction using your pre-trained model
+        predictions = model.predict(np.expand_dims(img, axis=0))
+
+        # Convert prediction indices to class labels
+        predicted_class_index = np.argmax(predictions)
+        predicted_class_label = class_labels.get(predicted_class_index, "Unknown")
+
+        # You can format the predictions as needed and return them
+        result = {
+            "predicted_class": predicted_class_label,
+            "class_probabilities": predictions.tolist(),
+        }
+
+        return result
+
+    except Exception as e:
+        return {"error": str(e)}
+
+if __name__ == "__main__":
+    uvicorn.run(app, port=8000)