xray.py

from flask import Flask, request, jsonify
from tensorflow.keras.models import load_model
import numpy as np
import cv2
import requests
from io import BytesIO
from flask_cors import CORS

# Load the model
model = load_model(r"xray_pneumonia_model.keras")

# Initialize Flask app
app = Flask(__name__)
CORS(app)

# Define the prediction function
def predict_xray(image_source):
    # Check if the source is a URL
    if image_source.startswith("http"):
        response = requests.get(image_source)
        if response.status_code != 200:
            raise ValueError("Failed to fetch image from URL")
        image_array = np.asarray(bytearray(response.content), dtype=np.uint8)
        img = cv2.imdecode(image_array, cv2.IMREAD_COLOR)
    else:
        img = cv2.imread(image_source)
        if img is None:
            raise ValueError("Image not found or invalid file path")

    # Preprocess the image
    img = cv2.resize(img, (150, 150))
    img = img / 255.0
    img = np.expand_dims(img, axis=0)

    # Make a prediction
    prediction = model.predict(img)[0][0]
    return "Pneumonia" if prediction > 0.5 else "Normal"


from groq import Groq
API_KEY_LLAMA = "cb95a6b8-29ef-4c5c-bd98-3fe6df21c3cd"

def generate_llama_response(result,file_path):
    predefined_prompt=f"You are a X-ray Analyzer you are being provided with X-ray image{file_path} and reslut of it {result} like 'normal' or 'penumonia' you have to assis thye user if penumonia detetcted in just 2 lines "
    client = Groq(api_key=API_KEY_LLAMA)
    response = client.chat.completions.create(
        messages=[{"role": "user", "content": predefined_prompt}],
        model="llama3-8b-8192",
    )
    return response.choices[0].message.content


# Define the /model route
@app.route('/model', methods=['POST'])
def model_predict():
    # Get JSON data from the request body
    data = request.get_json()

    # Check if the file path is provided
    if not data or 'file_path' not in data:
        return jsonify({"error": "No file path or URL provided"}), 400

    file_path = data['file_path']

    # Make a prediction
    try:
        result = predict_xray(file_path)
        response=generate_llama_response(result,file_path)
        return jsonify({"prediction": response})
    except Exception as e:
        return jsonify({"error": str(e)}), 500




if __name__ == '__main__':
    app.run(host='0.0.0.0', port=8002)