Update app.py

app.py

@@ -2,7 +2,6 @@ import gradio as gr
 from PIL import Image
 import torch
 from torchvision import models, transforms
-import fitz  # PyMuPDF
 import logging
 import os
 
@@ -10,7 +9,7 @@ import os
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
-# Condition list
+# Condition list (add your specific diseases here)
 conditions = [
     "Normal", "Pneumonia", "Cancer", "TB", "Other",
     "Coronary Artery Disease", "Aortic Aneurysm", "Stroke", "Peripheral Artery Disease",
@@ -20,7 +19,7 @@ conditions = [
     "Appendicitis", "Gallstones", "Kidney Stones", "Infections", "Abdominal Aortic Aneurysm", "Diverticulitis"
 ]
 
-# Condition details
+# Condition details for diagnosis (can be expanded with real data)
 condition_details = {
     "Normal": {"description": "No abnormal signs detected.", "recommendation": "Routine check-ups recommended."},
     "Pneumonia": {"description": "Lung inflammation detected, possibly infectious.", "recommendation": "Seek medical attention for treatment."},
@@ -49,19 +48,19 @@ condition_details = {
     "Diverticulitis": {"description": "Inflammation of diverticula in the colon.", "recommendation": "Gastroenterology consultation."}
 }
 
-# Load model (using a specialized X-ray model or pre-trained general model)
-model = models.densenet121(pretrained=True)  # You can swap to a more specific X-ray model if available
+# Load a pre-trained DenseNet121 model
+model = models.densenet121(pretrained=True)
 model.classifier = torch.nn.Linear(model.classifier.in_features, len(conditions))  # Adjust the classifier for our condition count
 model.eval()
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 model.to(device)
 
-# Image preprocessing
+# Image preprocessing function
 def preprocess_image(image):
     transform = transforms.Compose([
         transforms.Resize((224, 224)),  # Resize to match the model input size
         transforms.ToTensor(),
-        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])  # Standardize based on ImageNet values
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])  # Standard ImageNet normalization
     ])
     return transform(image).unsqueeze(0).to(device)
 
@@ -75,9 +74,11 @@ def predict_xray(image):
         with torch.no_grad():
             output = model(img_tensor)
 
+        # Get probabilities for each condition
         probs = torch.nn.functional.softmax(output, dim=1)[0]
         results = {conditions[i]: probs[i].item() * 100 for i in range(len(conditions))}
 
+        # Identify the condition with the highest probability
         top_condition = max(results, key=results.get)
         confidence = results[top_condition]
 
@@ -92,6 +93,7 @@ def predict_xray(image):
             </div>
             """
 
+        # Fetch details for the identified condition
         info = condition_details.get(top_condition, condition_details["Other"])
         return f"""
         <div style="font-family:Arial">
@@ -106,34 +108,10 @@ def predict_xray(image):
         logger.error(f"Error in prediction: {e}")
         return f"Error: {str(e)}"
 
-# Analyze PDF report
-def analyze_report(file):
-    if not file or not file.name.endswith(".pdf"):
-        return "Please upload a valid PDF file."
-    try:
-        doc = fitz.open(file.name)
-        text = "".join(page.get_text() for page in doc)
-        doc.close()
-
-        condition, disease, status = "Unclear", "Unknown", "Pending"
-
-        if "stroke" in text.lower():
-            condition, disease, status = "Stroke", "Brain Disorder", "Urgent Care Needed"
-        elif "cancer" in text.lower():
-            condition, disease, status = "Cancer", "Malignant Growth", "Consult Oncologist"
-        elif "fracture" in text.lower():
-            condition, disease, status = "Fracture", "Bone Injury", "Orthopedic Attention Required"
-
-        preview = text[:300] + "..." if text else "No readable content."
-        return f"Condition: {condition}\nDisease: {disease}\nStatus: {status}\n\nPreview:\n{preview}"
-
-    except Exception as e:
-        return f"Failed to process PDF: {str(e)}"
-
 # Gradio interface
 def create_interface():
     with gr.Blocks() as demo:
-        gr.Markdown("<h1 style='text-align:center;'>🩻 RadiologyScan AI</h1><p style='text-align:center;'>AI-powered X-ray and Report Analysis</p>")
+        gr.Markdown("<h1 style='text-align:center;'>🩻 RadiologyScan AI</h1><p style='text-align:center;'>AI-powered X-ray Analysis</p>")
 
         with gr.Tabs():
             with gr.TabItem("X-ray Analysis"):
@@ -143,14 +121,7 @@ def create_interface():
                     gr.Button("Analyze X-ray", elem_id="analyze_button", scale=0.3).click(predict_xray, inputs=img_input, outputs=summary_output)
                     gr.Button("Clear", elem_id="clear_button", scale=0.3).click(lambda: [None, ""], inputs=None, outputs=[img_input, summary_output])
 
-            with gr.TabItem("Report Analysis"):
-                pdf_input = gr.File(label="Upload PDF Report", file_types=[".pdf"])
-                summary_output_report = gr.Textbox(label="Summary Result", lines=5)
-                with gr.Row():
-                    gr.Button("Analyze Report", elem_id="analyze_button", scale=0.3).click(analyze_report, inputs=pdf_input, outputs=summary_output_report)
-                    gr.Button("Clear", elem_id="clear_button", scale=0.3).click(lambda: [None, ""], inputs=None, outputs=[pdf_input, summary_output_report])
-
-    return demo
+        return demo
 
 if __name__ == "__main__":
     demo = create_interface()