Delete main.py

main.py

@@ -1,35 +0,0 @@
-from fastapi import FastAPI, UploadFile, File
-import numpy as np
-from PIL import Image
-import io
-from tensorflow import keras
-
-# Load the model
-model = keras.models.load_model("model.h5")
-
-# Hardcoded class labels (ensure this matches your training order)
-class_labels = ['NORMAL', 'PNEUMONIA']
-
-# Initialize FastAPI app
-app = FastAPI()
-
-# Preprocessing function
-def preprocess_image(image_bytes):
-    image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
-    image = image.resize((224, 224))  # match model input size
-    img_array = np.array(image) / 255.0  # normalize
-    img_array = np.expand_dims(img_array, axis=0)  # add batch dimension
-    return img_array
-
-# Prediction endpoint
-@app.post("/predict")
-async def predict(file: UploadFile = File(...)):
-    image_bytes = await file.read()
-    img_array = preprocess_image(image_bytes)
-    predictions = model.predict(img_array)
-    predicted_class = class_labels[np.argmax(predictions)]
-    confidence = float(np.max(predictions))
-    return {
-        "prediction": predicted_class,
-        "confidence": confidence
-    }