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import gradio as gr
from PIL import Image
import torch
from torchvision import models, transforms
import PyPDF2 # For reading patient reports (PDFs)
import io
# Load the pre-trained model (for example, ResNet18)
model = models.resnet18(pretrained=True)
model.eval()
# Define image preprocessing function
def preprocess_image(image):
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
])
return transform(image).unsqueeze(0)
# Define a simple prediction function for X-ray images
def predict_xray(image):
image_tensor = preprocess_image(image)
with torch.no_grad():
outputs = model(image_tensor)
probs = torch.nn.functional.softmax(outputs[0], dim=0)
# Example output - replace with actual classes based on your model
conditions = ["Normal", "Pneumonia", "Cancer", "TB", "Other"]
results = {conditions[i]: float(probs[i]) for i in range(len(conditions))}
summary = f"Summary: Based on the X-ray, the patient is diagnosed with: {max(results, key=results.get)}"
return summary, results
# Define a function to read and analyze patient reports (PDFs)
def analyze_report(file):
text = ""
if file.name.endswith(".pdf"):
pdf_reader = PyPDF2.PdfReader(file)
for page in pdf_reader.pages:
text += page.extract_text()
# You can process the extracted text and provide insights
# For now, let's assume the text contains diagnosis
report_summary = f"Patient Report: {text[:300]}..." # First 300 characters of report as preview
return report_summary
# Gradio Interface
def create_interface():
with gr.Blocks() as demo:
# Custom CSS for UI
custom_css = """
.gradio-container {
background-color: #f4f6f9;
border-radius: 15px;
box-shadow: 0 4px 15px rgba(0, 0, 0, 0.1);
padding: 30px;
font-family: 'Segoe UI', sans-serif;
}
.title {
font-size: 30px;
text-align: center;
color: #4C6A92;
}
.gradio-button {
background-color: #3B82F6;
color: white;
border-radius: 10px;
padding: 15px;
}
.result-box {
background-color: #ffffff;
border-radius: 10px;
padding: 20px;
box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
margin-top: 20px;
}
"""
# Title section
gr.Markdown("<h1 class='title'>RadiologyScan AI</h1>")
# Upload X-ray image section
with gr.Row():
xray_input = gr.Image(label="Upload Chest X-ray", type="pil")
report_input = gr.File(label="Upload Patient Report (PDF)", file_count="single")
# Buttons for analysis
with gr.Row():
predict_button = gr.Button("Analyze X-ray", elem_classes="gradio-button")
report_button = gr.Button("Analyze Report", elem_classes="gradio-button")
# Results section for the X-ray image
xray_output = gr.Textbox(label="X-ray Summary", interactive=False, elem_classes="result-box")
xray_result = gr.JSON(label="X-ray Results", interactive=False, elem_classes="result-box")
# Results section for the patient report
report_output = gr.Textbox(label="Report Summary", interactive=False, elem_classes="result-box")
# Event handlers for buttons
predict_button.click(predict_xray, inputs=xray_input, outputs=[xray_output, xray_result])
report_button.click(analyze_report, inputs=report_input, outputs=report_output)
return demo
# Launch the Gradio interface
demo = create_interface()
demo.launch(share=True)
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