app.py

import os
import streamlit as st
from PIL import Image
import torch
from torchvision import transforms, models

# Set up environment variable for Groq API
os.environ["GROQ_API_KEY"] = "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"

# Load Pretrained Model for Organ Recognition
@st.cache_resource
def load_organ_model():
    model = models.resnet18(pretrained=True)  # ResNet18 pretrained model
    model.fc = torch.nn.Linear(model.fc.in_features, 4)  # Modify for 4 classes
    model.eval()
    return model

organ_model = load_organ_model()

# Image Preprocessing
def preprocess_image(image):
    transform = transforms.Compose([
        transforms.Resize((224, 224)),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ])
    return transform(image).unsqueeze(0)

# Organ Recognition Prediction
def predict_organ(image):
    with torch.no_grad():
        input_tensor = preprocess_image(image)
        output = organ_model(input_tensor)
        classes = ["Lungs", "Heart", "Spine", "Other"]  # Example organ classes
        prediction = classes[output.argmax().item()]
    return prediction

# Streamlit App
st.title("X-ray Organ Recognition App")
st.sidebar.title("Navigation")
task = st.sidebar.radio("Select a task", ["Upload X-ray", "AI Insights"])

if task == "Upload X-ray":
    uploaded_file = st.file_uploader("Upload an X-ray image", type=["jpg", "png", "jpeg"])

    if uploaded_file:
        image = Image.open(uploaded_file)
        st.image(image, caption="Uploaded X-ray", use_column_width=True)

        # Predict Organ
        st.subheader("Step 1: Identify the Organ in the X-ray")
        organ = predict_organ(image)
        st.write(f"Predicted Organ: **{organ}**")

elif task == "AI Insights":
    st.subheader("Ask AI")
    user_input = st.text_area("Enter your query for AI insights")
    if user_input:
        st.write("AI insights will be generated here.")  # Placeholder for AI logic