import os
import streamlit as st
import tensorflow as tf
from tensorflow.keras.applications import ResNet50
from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Dense, GlobalAveragePooling2D
from tensorflow.keras.preprocessing.image import img_to_array, load_img
import numpy as np
import requests
from PIL import Image

# Set Groq API key in environment variable
os.environ['GROQ_API_KEY'] = "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"
GROQ_API_KEY = os.getenv('GROQ_API_KEY')

# Load pre-trained ResNet50 for normal/abnormal classification
base_model = ResNet50(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(1024, activation='relu')(x)
predictions = Dense(1, activation='sigmoid')(x)
classification_model = Model(inputs=base_model.input, outputs=predictions)

# Load pre-trained ResNet50 for organ recognition
organ_model = ResNet50(weights='imagenet')

def classify_image(image_path):
    """Classify the image as normal or abnormal."""
    image = load_img(image_path, target_size=(224, 224))
    image_array = img_to_array(image)
    image_array = preprocess_input(image_array)
    image_array = np.expand_dims(image_array, axis=0)
    prediction = classification_model.predict(image_array)
    return 'Abnormal' if prediction[0][0] > 0.5 else 'Normal'

def recognize_organ(image_path):
    """Recognize the organ in the image."""
    image = load_img(image_path, target_size=(224, 224))
    image_array = img_to_array(image)
    image_array = preprocess_input(image_array)
    image_array = np.expand_dims(image_array, axis=0)
    prediction = organ_model.predict(image_array)
    decoded = decode_predictions(prediction, top=3)[0]
    return decoded[0][1]  # Top predicted class

def get_ai_insights(organ):
    """Fetch AI-based insights about the organ using Groq API."""
    url = "https://api.groq.com/v1/insights"
    headers = {"Authorization": f"Bearer {GROQ_API_KEY}", "Content-Type": "application/json"}
    data = {"query": f"Provide detailed insights about {organ} X-ray, its diseases, and treatments."}
    response = requests.post(url, headers=headers, json=data)
    if response.status_code == 200:
        return response.json().get("insights", "No insights available.")
    else:
        return "Failed to fetch insights. Please try again later."

def main():
    st.title("Medical Image Classification App")
    st.sidebar.title("Navigation")
    
    uploaded_file = st.file_uploader("Upload an X-ray or MRI image", type=["jpg", "jpeg", "png"])

    if uploaded_file:
        image = Image.open(uploaded_file)
        st.image(image, caption="Uploaded Image", use_column_width=True)
        
        with open("temp_image.jpg", "wb") as f:
            f.write(uploaded_file.getbuffer())

        st.write("### Classification Result")
        result = classify_image("temp_image.jpg")
        st.write(f"The X-ray is classified as: **{result}**")

        st.write("### Organ Recognition")
        organ = recognize_organ("temp_image.jpg")
        st.write(f"Recognized Organ: **{organ}**")

        st.write("### AI-Based Insights")
        insights = get_ai_insights(organ)
        st.write(insights)

if __name__ == "__main__":
    main()