Update app.py

app.py

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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)