| | from fastapi import FastAPI, File, UploadFile, HTTPException
|
| | from fastapi.responses import JSONResponse
|
| | from PIL import Image
|
| | import numpy as np
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| | import tensorflow as tf
|
| | import requests
|
| | import io
|
| |
|
| | app = FastAPI(title="رتینوپاتی دیابتی API", description="تشخیص ۵ مرحله DR با مدل CNN")
|
| |
|
| |
|
| | MODEL_URL = "https://huggingface.co/megsciip/eye1/resolve/main/eye_modelv2.tflite"
|
| | MODEL_PATH = "eye_modelv2.tflite"
|
| |
|
| | def download_model():
|
| | if not tf.io.gfile.exists(MODEL_PATH):
|
| | print("در حال دانلود مدل...")
|
| | response = requests.get(MODEL_URL)
|
| | response.raise_for_status()
|
| | with open(MODEL_PATH, "wb") as f:
|
| | f.write(response.content)
|
| | print("مدل دانلود شد!")
|
| |
|
| | download_model()
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| |
|
| |
|
| | interpreter = tf.lite.Interpreter(model_path=MODEL_PATH)
|
| | interpreter.allocate_tensors()
|
| | input_details = interpreter.get_input_details()
|
| | output_details = interpreter.get_output_details()
|
| |
|
| | IMG_SIZE = (224, 224)
|
| | CLASS_NAMES = [
|
| | "۰ - بدون رتینوپاتی (چشم سالم)",
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| | "۱ - رتینوپاتی خفیف (Mild DR)",
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| | "۲ - رتینوپاتی متوسط (Moderate DR)",
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| | "۳ - رتینوپاتی شدید (Severe DR)",
|
| | "۴ - رتینوپاتی تکثیری (Proliferative DR)"
|
| | ]
|
| |
|
| | def preprocess_image(img: Image.Image):
|
| | img = img.resize(IMG_SIZE)
|
| | if img.mode != 'RGB':
|
| | img = img.convert('RGB')
|
| | img_array = np.array(img, dtype=np.float32)
|
| | img_array = np.expand_dims(img_array, axis=0)
|
| | img_array = tf.keras.applications.resnet50.preprocess_input(img_array)
|
| | return img_array
|
| |
|
| | @app.post("/predict")
|
| | async def predict(file: UploadFile = File(...)):
|
| | if not file.content_type.startswith("image/"):
|
| | raise HTTPException(400, detail="فقط فایل تصویری مجاز است!")
|
| |
|
| | try:
|
| | contents = await file.read()
|
| | img = Image.open(io.BytesIO(contents))
|
| |
|
| | input_data = preprocess_image(img)
|
| | interpreter.set_tensor(input_details[0]['index'], input_data)
|
| | interpreter.invoke()
|
| | predictions = interpreter.get_tensor(output_details[0]['index'])[0]
|
| |
|
| | predicted_idx = int(np.argmax(predictions))
|
| | confidence = float(predictions[predicted_idx])
|
| |
|
| | result = {
|
| | "diagnosis": CLASS_NAMES[predicted_idx],
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| | "confidence": f"{confidence:.1%}",
|
| | "probabilities": {
|
| | CLASS_NAMES[i].split(" - ")[1]: f"{float(predictions[i]):.1%}"
|
| | for i in range(len(predictions))
|
| | },
|
| | "recommended_action": "مشاوره با چشمپزشک ضروری است." if predicted_idx > 0 else "چشم سالم به نظر میرسد."
|
| | }
|
| |
|
| | return JSONResponse(result)
|
| |
|
| | except Exception as e:
|
| | raise HTTPException(500, detail=f"خطا در پردازش: {str(e)}")
|
| |
|
| | @app.get("/")
|
| | def home():
|
| | return {"message": "API تشخیص رتینوپاتی دیابتی فعال است! POST به /predict"}
|
| |
|
| |
|
| | if __name__ == "__main__":
|
| | import uvicorn
|
| | uvicorn.run(app, host="0.0.0.0", port=7860) |
