main.py

import fastapi
from fastapi import File, UploadFile, HTTPException
from fastapi.middleware.cors import CORSMiddleware # Added for frontend communication
import uvicorn
import pickle
import numpy as np
import tensorflow as tf
from PIL import Image
import io
import os

# --- Basic FastAPI App Setup ---
app = fastapi.FastAPI(
    title="X-ray Fracture Detection API",
    description="An API to predict bone fractures from X-ray images.",
    version="1.0.0"
)

# --- CORS (Cross-Origin Resource Sharing) Middleware ---
# This is the new section that fixes the "Failed to fetch" error.
# It allows your HTML frontend to communicate with this Python backend.
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],  # Allows all origins
    allow_credentials=True,
    allow_methods=["*"],  # Allows all methods (GET, POST, etc.)
    allow_headers=["*"],  # Allows all headers
)

# --- Loading the Model and Class Indices ---
# Note: Ensure these .pkl files are in the same directory as this script.
MODEL_PATH = 'fracture_detection_model.pkl'
CLASS_INDICES_PATH = 'class_indices.pkl'

# Check if model files exist before loading
if not os.path.exists(MODEL_PATH) or not os.path.exists(CLASS_INDICES_PATH):
    raise RuntimeError(f"Model or class indices files not found. Please ensure '{MODEL_PATH}' and '{CLASS_INDICES_PATH}' are in the correct directory.")

# Load the trained model
try:
    with open(MODEL_PATH, 'rb') as f:
        model = pickle.load(f)
except Exception as e:
    raise RuntimeError(f"Error loading the model: {e}")

# Load the class indices
try:
    with open(CLASS_INDICES_PATH, 'rb') as f:
        class_indices = pickle.load(f)
    # Invert the dictionary to map index to class name
    class_names = {v: k for k, v in class_indices.items()}
except Exception as e:
    raise RuntimeError(f"Error loading class indices: {e}")

print("--- Model and class indices loaded successfully! ---")

# --- Image Preprocessing Function ---
def preprocess_image(image_bytes: bytes, target_size=(150, 150)) -> np.ndarray:
    """
    Preprocesses the uploaded image to match the model's input requirements.
    """
    try:
        # Open the image from bytes
        img = Image.open(io.BytesIO(image_bytes))

        # Ensure image is in RGB format
        if img.mode != "RGB":
            img = img.convert("RGB")

        # Resize the image
        img = img.resize(target_size)

        # Convert image to numpy array and scale pixel values
        img_array = tf.keras.preprocessing.image.img_to_array(img)
        img_array = img_array / 255.0

        # Expand dimensions to create a batch of 1
        img_batch = np.expand_dims(img_array, axis=0)
        return img_batch
    except Exception as e:
        # Raise an HTTPException for bad image data
        raise HTTPException(status_code=400, detail=f"Image preprocessing failed: {e}")


# --- Prediction Endpoint ---
@app.post("/predict")
async def predict(file: UploadFile = File(...)):
    """
    Accepts an X-ray image file and returns the predicted fracture type.
    """
    # 1. Read the image file uploaded by the user
    image_bytes = await file.read()

    # 2. Preprocess the image to prepare it for the model
    processed_image = preprocess_image(image_bytes)

    # 3. Make a prediction using the loaded model
    prediction = model.predict(processed_image)

    # 4. Process the prediction result
    # Find the index of the highest probability
    predicted_index = np.argmax(prediction[0])
    # Get the corresponding class name
    predicted_class = class_names[predicted_index]
    # Get the confidence score
    confidence = float(prediction[0][predicted_index])

    # 5. Return the result in a JSON format
    return {
        "prediction": predicted_class,
        "confidence": f"{confidence:.2f}"
    }

# --- Root Endpoint ---
@app.get("/")
def read_root():
    return {"message": "Welcome to the Fracture Detection API. Please use the /docs endpoint for more information."}

# --- To run this application ---
# 1. Install necessary libraries: pip install fastapi "uvicorn[standard]" tensorflow numpy Pillow python-multipart
# 2. Save your .pkl files in the same directory as this script.
# 3. Run from your terminal: uvicorn main:app --reload