Upload 3 files

upload file model

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+Trained_after_EFF0.keras filter=lfs diff=lfs merge=lfs -text

Trained_after_EFF0.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:95c1692e95d27ee9f3893619bdf784df523f35d13d57e440151d164a7a782776
+size 39075502

main.py

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+from fastapi import FastAPI, UploadFile, File
+from fastapi.responses import JSONResponse
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.encoders import jsonable_encoder
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing.image import load_img, img_to_array
+from PIL import Image
+
+import tensorflow.keras.backend as K
+import os
+import uvicorn
+import numpy as np
+
+# Initialize FastAPI app
+app = FastAPI()
+origins = ['*']
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=origins,
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Custom F1 score function
+def f1_score(y_true, y_pred):
+    precision = K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) / K.maximum(
+        K.sum(K.round(K.clip(y_pred, 0, 1))), K.epsilon()
+    )
+    recall = K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) / K.maximum(
+        K.sum(K.round(K.clip(y_true, 0, 1))), K.epsilon()
+    )
+    return 2 * (precision * recall) / (precision + recall + K.epsilon())
+
+# Load model
+MODEL_PATH = "Trained_after_EFF0.keras"  # Ensure the path is correct
+model = load_model(MODEL_PATH, custom_objects={'f1_score': f1_score})
+
+# Image size for the model
+IMAGE_SIZE = 224
+
+# Preprocess image
+def preprocess_image(image_path, target_size):
+    image = load_img(image_path, target_size=(target_size, target_size))
+    image_array = img_to_array(image)
+    image_array = np.expand_dims(image_array, axis=0)
+    return image_array
+
+# API to predict image
+@app.post("/predict")
+async def predict_image(file: UploadFile = File(...)):
+    try:
+        upload_dir = "./uploads"
+        os.makedirs(upload_dir, exist_ok=True)
+        file_path = os.path.join(upload_dir, file.filename)
+        with open(file_path, "wb") as buffer:
+            buffer.write(await file.read())
+
+        image_array = preprocess_image(file_path, target_size=IMAGE_SIZE)
+
+        prediction = model.predict(image_array)
+        predicted_label = int(np.argmax(prediction))
+        confidence = float(np.max(prediction))
+
+        os.remove(file_path)
+
+        return JSONResponse(
+            content=jsonable_encoder(
+                {"predicted_label": predicted_label, "confidence": confidence}
+            )
+        )
+    except Exception as e:
+        return JSONResponse(content=jsonable_encoder({"error": str(e)}), status_code=500)
+
+# Run FastAPI server
+if __name__ == '__main__':
+    uvicorn.run(app, host="0.0.0.0", port=8002)

requirements.txt

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+fastapi
+uvicorn
+pyngrok
+tensorflow
+keras
+python-multipart
+Pillow