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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
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