Create main.py

main.py

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+import os
+import numpy as np
+import tensorflow as tf
+from fastapi import FastAPI, File, UploadFile
+from fastapi.responses import JSONResponse
+from io import BytesIO
+from PIL import Image
+from tensorflow.keras.preprocessing.image import img_to_array
+from tensorflow.keras.applications import resnet50
+from tensorflow.keras.applications.resnet50 import preprocess_input
+import uvicorn
+
+# Initialize FastAPI app
+app = FastAPI()
+
+# Model and class information
+model_path = "model.keras"
+class_indices = {0: 'glaucoma', 1: 'normal'}
+
+# Load the model if it exists
+if os.path.exists(model_path):
+    model = tf.keras.models.load_model(model_path)
+    print("Model loaded successfully.")
+else:
+    print(f"Model file not found at {model_path}. Please upload the model.")
+
+# Function to predict glaucoma in an image and return the class name
+def predict_image(image_data):
+    try:
+        # Load the image from binary data
+        img = Image.open(BytesIO(image_data))
+        # Resize the image to the target size
+        img = img.resize((224, 224))
+        # Convert image to array format for the model
+        img_array = img_to_array(img)
+        img_array = np.expand_dims(img_array, axis=0)
+        img_array = preprocess_input(img_array)
+
+        # Make prediction
+        prediction = model.predict(img_array)
+        predicted_class = np.argmax(prediction[0])
+        class_name = class_indices[predicted_class]  # Map to class name
+        return class_name
+    except Exception as e:
+        print("Prediction error:", e)
+        return "Error during prediction"
+
+# Route for health check
+@app.get("/health")
+async def api_health_check():
+    return JSONResponse(content={"status": "Service is running"})
+
+# Route for prediction using image via API
+@app.post("/predict")
+async def api_predict_image(file: UploadFile = File(...)):
+    try:
+        # Read the image file as binary data
+        image_data = await file.read()
+        
+        # Call the prediction function with the image data
+        prediction = predict_image(image_data)
+        
+        return JSONResponse(content={"prediction": prediction})
+    except Exception as e:
+        return JSONResponse(content={"error": str(e)})
+
+# Run the FastAPI app
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=7860)