Create main.py

main.py

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+from fastapi import FastAPI, File, UploadFile
+from fastapi.responses import JSONResponse
+import tensorflow as tf
+import numpy as np
+import pickle
+from PIL import Image
+import io
+
+app = FastAPI()
+
+# Load model and encoder
+model = tf.keras.models.load_model("best_sleeve_model.keras")
+with open("sleeve_length_encoder.pkl", "rb") as f:
+    encoder = pickle.load(f)
+
+# Define image preprocessing function
+def preprocess_image(image_bytes):
+    image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
+    image = image.resize((224, 224))  # Change this if your model expects a different size
+    image_array = np.array(image) / 255.0  # Normalize if your model was trained that way
+    image_array = np.expand_dims(image_array, axis=0)  # Add batch dimension
+    return image_array
+
+@app.get("/")
+def root():
+    return {"message": "Sleeve Length Image Prediction API is running."}
+
+@app.post("/predict")
+async def predict(file: UploadFile = File(...)):
+    try:
+        image_bytes = await file.read()
+        input_tensor = preprocess_image(image_bytes)
+
+        # Make prediction
+        prediction = model.predict(input_tensor)
+        class_index = np.argmax(prediction, axis=1)
+        label = encoder.inverse_transform(class_index)
+
+        return JSONResponse(content={"sleeve_length": label[0]})
+    except Exception as e:
+        return JSONResponse(content={"error": str(e)}, status_code=500)