Update app.py

app.py

@@ -7,10 +7,10 @@ import logging
 import os
 
 # Set up logging
-logging.basicConfig(level=logging.DEBUG)  # Set to DEBUG for detailed output
+logging.basicConfig(level=logging.DEBUG)
 logger = logging.getLogger(__name__)
 
-# Define conditions
+# Define conditions based on provided radiology information
 conditions = [
     "Normal", "Pneumonia", "Cancer", "TB", "Other",
     "Coronary Artery Disease", "Aortic Aneurysm", "Stroke", "Peripheral Artery Disease",
@@ -20,71 +20,28 @@ conditions = [
     "Appendicitis", "Gallstones", "Kidney Stones", "Infections", "Abdominal Aortic Aneurysm", "Diverticulitis"
 ]
 
-# Define condition details for all conditions
-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."},
-    "Cancer": {"description": "Suspicious masses suggest cancer; further imaging needed.", "recommendation": "Consult an oncologist."},
-    "TB": {"description": "Cavitary lesions indicate tuberculosis.", "recommendation": "Immediate medical evaluation required."},
-    "Other": {"description": "Unclear abnormality; further investigation needed.", "recommendation": "Consult a radiologist."},
-    "Coronary Artery Disease": {"description": "Narrowing of coronary arteries detected.", "recommendation": "Cardiology consultation required."},
-    "Aortic Aneurysm": {"description": "Abnormal enlargement of the aorta.", "recommendation": "Vascular surgery evaluation."},
-    "Stroke": {"description": "Signs of brain ischemia or hemorrhage.", "recommendation": "Urgent neurological evaluation."},
-    "Peripheral Artery Disease": {"description": "Reduced blood flow in peripheral arteries.", "recommendation": "Vascular specialist consultation."},
-    "Brain Tumor": {"description": "Abnormal mass in the brain detected.", "recommendation": "Consult a neurosurgeon."},
-    "Alzheimer's Disease": {"description": "Signs of neurodegenerative changes.", "recommendation": "Neurology consultation."},
-    "Multiple Sclerosis": {"description": "Demyelinating lesions in the CNS.", "recommendation": "Neurology consultation."},
-    "Epilepsy": {"description": "Signs of seizure activity.", "recommendation": "Neurology consultation."},
-    "COPD": {"description": "Lung damage from COPD observed.", "recommendation": "Pulmonary consultation."},
-    "Lung Cancer": {"description": "Malignant lung masses detected.", "recommendation": "Oncology consultation."},
-    "Pulmonary Embolism": {"description": "Blockage in pulmonary arteries.", "recommendation": "Urgent medical attention."},
-    "Fractures": {"description": "Bone break detected.", "recommendation": "Orthopedic evaluation."},
-    "Arthritis": {"description": "Joint inflammation detected.", "recommendation": "Rheumatology consultation."},
-    "Osteoporosis": {"description": "Reduced bone density observed.", "recommendation": "Bone health specialist consultation."},
-    "Appendicitis": {"description": "Inflammation of the appendix.", "recommendation": "Surgical evaluation."},
-    "Gallstones": {"description": "Stones in the gallbladder detected.", "recommendation": "Gastroenterology consultation."},
-    "Kidney Stones": {"description": "Stones in the kidneys detected.", "recommendation": "Urology consultation."},
-    "Infections": {"description": "Signs of infection observed.", "recommendation": "Infectious disease consultation."},
-    "Abdominal Aortic Aneurysm": {"description": "Enlargement of the abdominal aorta.", "recommendation": "Vascular surgery evaluation."},
-    "Diverticulitis": {"description": "Inflammation of diverticula in the colon.", "recommendation": "Gastroenterology consultation."}
-}
-
 # Load and configure the model
-try:
-    model = models.densenet121(weights="IMAGENET1K_V1")
-    num_features = model.classifier.in_features
-    model.classifier = torch.nn.Linear(num_features, len(conditions))
-    model.eval()
-except AttributeError:
-    model = models.densenet121(pretrained=True)
-    num_features = model.classifier.in_features
-    model.classifier = torch.nn.Linear(num_features, len(conditions))
-    model.eval()
+model = models.densenet121(weights="IMAGENET1K_V1")  # DenseNet pre-trained on ImageNet
+num_features = model.classifier.in_features
+model.classifier = torch.nn.Linear(num_features, len(conditions))  # Output for all 24 conditions
+model.eval()
 
 # Define device
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 model = model.to(device)
-logger.info(f"Using device: {device}")
 
-# Load model state if available
-model_path = os.getenv("MODEL_PATH", "xray_model.pth")
+# Load model state if available, otherwise initialize
+model_path = "xray_model.pth"
 if os.path.exists(model_path):
-    try:
-        model.load_state_dict(torch.load(model_path, map_location=device))
-        logger.info(f"Loaded model from {model_path}")
-    except Exception as e:
-        logger.warning(f"Failed to load model from {model_path}: {str(e)}. Using random weights.")
+    model.load_state_dict(torch.load(model_path))
+    logger.info(f"Loaded model from {model_path}")
 else:
     logger.info("No pre-trained model found. Initializing with random weights. Training required.")
 
 # Define image preprocessing function
 def preprocess_image(image):
-    if not isinstance(image, Image.Image):
-        logger.error("Invalid image format. Expected PIL Image.")
-        raise ValueError("Uploaded file is not a valid image.")
-    
     transform = transforms.Compose([
-        transforms.Resize((224, 224)),
+        transforms.Resize((224, 224)),  # Resize to fit the model input size
         transforms.ToTensor(),
         transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
     ])
@@ -92,7 +49,7 @@ def preprocess_image(image):
     logger.debug(f"Preprocessed image tensor shape: {image_tensor.shape}")
     return image_tensor
 
-# Define prediction function
+# Define prediction function with detailed output and error handling
 def predict_xray(image):
     try:
         if image is None:
@@ -101,21 +58,40 @@ def predict_xray(image):
         image_tensor = preprocess_image(image)
         with torch.no_grad():
             outputs = model(image_tensor)
-        probs = torch.nn.functional.softmax(outputs, dim=1)[0]
+        probs = torch.nn.functional.softmax(outputs, dim=1)[0]  # Softmax over all conditions
         results = {conditions[i]: float(probs[i].cpu().numpy()) * 100 for i in range(len(conditions))}
 
+        # Handle case where predicted class might not be in our list of conditions
         most_likely_condition = max(results, key=results.get)
-        confidence = results[most_likely_condition]
-
-        if most_likely_condition not in condition_details:
+        
+        # Ensure the predicted condition is in the valid conditions list
+        if most_likely_condition not in conditions:
             most_likely_condition = "Other"
             confidence = 0.0
+        else:
+            confidence = results[most_likely_condition]
 
+        # Create a detailed summary of results
         summary = f"**Summary**: Based on the X-ray analysis, the most likely diagnosis is: <b>{most_likely_condition}</b> with a confidence of <b>{confidence:.2f}%</b>."
+
+        # Enhanced condition details for each disease/condition
+        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."},
+            "Cancer": {"description": "Suspicious masses suggest cancer; further imaging needed.", "recommendation": "Consult an oncologist."},
+            "TB": {"description": "Cavitary lesions indicate tuberculosis.", "recommendation": "Immediate medical evaluation required."},
+            "Other": {"description": "Unclear abnormality; further investigation needed.", "recommendation": "Consult a radiologist."},
+            "Fractures": {"description": "Bone break detected.", "recommendation": "Orthopedic evaluation."},
+            "COPD": {"description": "Lung damage from COPD observed.", "recommendation": "Pulmonary consultation."},
+            # Add the rest of the conditions as needed...
+        }
+
+        # Display results in a clear format
         detailed_results = "<ul class='result-list'>"
         for condition, prob in results.items():
             detailed_results += f"<li><b>{condition}:</b> {prob:.2f}%</li>"
         detailed_results += "</ul>"
+
         additional_feedback = f"<div class='feedback-box'><b>Description:</b> {condition_details[most_likely_condition]['description']}<br><b>Recommendation:</b> {condition_details[most_likely_condition]['recommendation']}</div>"
 
         logger.info(f"Prediction: {most_likely_condition} with confidence {confidence:.2f}%")
@@ -125,82 +101,66 @@ def predict_xray(image):
         logger.error(f"Error in predict_xray: {str(e)}")
         return f"Error: {str(e)}", "", ""
 
-# Enhanced function to analyze patient reports (PDFs)
+# Enhanced function to analyze patient reports (PDFs) using PyMuPDF (fitz)
 def analyze_report(file):
-    if not file or not file.name.endswith(".pdf"):
-        return "Please upload a valid PDF file."
-
     text = ""
     patient_condition = "Unclear"
     disease = "Unknown"
     status = "Pending further tests"
 
-    try:
-        pdf_reader = fitz.open(file.name)
-        for page in pdf_reader:
-            text += page.get_text("text")
-        pdf_reader.close()
-
-        if "stroke" in text.lower():
-            patient_condition = "Stroke"
-            disease = "Brain Disorder"
-            status = "Urgent Care Needed"
-        elif "cancer" in text.lower():
-            patient_condition = "Cancer"
-            disease = "Malignant Growth"
-            status = "Consult Oncologist"
-        elif "fracture" in text.lower():
-            patient_condition = "Fracture"
-            disease = "Bone Injury"
-            status = "Orthopedic Attention Required"
-
-        report_summary = f"Patient's Condition: {patient_condition}\nDisease: {disease}\nCondition Status: {status}\n\nReport Preview: {text[:300]}..." if text else "No readable text found in the PDF."
-        return report_summary
-
-    except Exception as e:
-        logger.error(f"Error reading PDF: {str(e)}")
-        return f"Error processing PDF: {str(e)}"
-
-# Gradio Interface with Tabs
+    if file and file.name.endswith(".pdf"):
+        try:
+            # Open the PDF using PyMuPDF
+            pdf_reader = fitz.open(file.name)
+            for page in pdf_reader:
+                text += page.get_text("text")  # Extract text from each page
+
+            # Example: Let's search for conditions in the text
+            if "stroke" in text.lower():
+                patient_condition = "Stroke"
+                disease = "Brain Disorder"
+                status = "Urgent Care Needed"
+            elif "cancer" in text.lower():
+                patient_condition = "Cancer"
+                disease = "Malignant Growth"
+                status = "Consult Oncologist"
+            elif "fracture" in text.lower():
+                patient_condition = "Fracture"
+                disease = "Bone Injury"
+                status = "Orthopedic Attention Required"
+            # You can add more conditions here based on keyword matching
+            
+            report_summary = f"Patient's Condition: {patient_condition}\nDisease: {disease}\nCondition Status: {status}\n\nReport Preview: {text[:300]}..." if text else "No readable text found in the PDF."
+        
+        except Exception as e:
+            logger.error(f"Error reading PDF: {str(e)}")
+            report_summary = f"Error processing PDF: {str(e)}"
+    else:
+        report_summary = "Please upload a valid PDF file."
+
+    return report_summary
+
+# Gradio Interface with enhanced UI using Tabs
 def create_interface():
-    logger.debug("Initializing Gradio interface")
-    # Minimal CSS to avoid styling issues
-    custom_css = """
-    .title { font-size: 30px; text-align: center; color: #4C6A92; }
-    .subtitle { text-align: center; color: #666; }
-    """
-    with gr.Blocks(css=custom_css) as demo:
+    with gr.Blocks() as demo:
+        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; margin-bottom: 20px; }
+        .gradio-button { background-color: #3B82F6; color: white; border-radius: 10px; padding: 15px 30px; font-size: 16px; transition: background-color 0.3s; }
+        .gradio-button:hover { background-color: #2563EB; }
+        .result-box { background-color: #ffffff; border-radius: 10px; padding: 20px; box-shadow: 0 4px 8px rgba(0,0,0,0.1); margin-top: 20px; max-width: 100%; }
+        .result-list { padding-left: 20px; margin: 10px 0; }
+        .result-summary { font-size: 18px; color: #2F4F4F; font-weight: 500; }
+        .feedback-box { background-color: #F0FFF4; padding: 10px; border-left: 4px solid #38A169; border-radius: 5px; margin-top: 10px; }
+        """
+        
         gr.Markdown("<h1 class='title'>RadiologyScan AI</h1>")
-        gr.Markdown("<p class='subtitle'>AI-powered analysis for X-rays and patient reports</p>")
-
-        with gr.Tabs():
-            with gr.TabItem("X-ray Analysis"):
-                image_input = gr.Image(label="Upload X-ray", type="pil")
-                output_summary = gr.HTML(label="Summary")
-                output_details = gr.HTML(label="Detailed Results")
-                output_feedback = gr.HTML(label="Additional Feedback")
-                gr.Button("Analyze X-ray").click(
-                    fn=predict_xray,
-                    inputs=image_input,
-                    outputs=[output_summary, output_details, output_feedback]
-                )
-
-            with gr.TabItem("Report Analysis"):
-                file_input = gr.File(label="Upload Patient Report (PDF)", file_count="single")
-                output_report = gr.Textbox(label="Report Summary", interactive=False)
-                gr.Button("Analyze Report").click(
-                    fn=analyze_report,
-                    inputs=file_input,
-                    outputs=output_report
-                )
-        logger.debug("Gradio interface initialized")
-    return demo
-
-if __name__ == "__main__":
-    logger.debug("Starting Gradio application")
-    try:
-        demo = create_interface()
-        demo.launch(server_port=7860, ssr_mode=False)  # Explicit port, disable SSR
-        logger.debug("Gradio application launched")
-    except Exception as e:
-        logger.error(f"Failed to launch Gradio application: {str(e)}")
+        gr.Markdown("<p style='text-align: center; color: #666;'>AI-powered analysis for X-rays and patient reports</p>")
+        
+        # Correctly provide interface_list to TabbedInterface
+        xray_tab = gr.Interface(fn=predict_xray, inputs=gr.Image(label="Upload X-ray", type="pil"), outputs=[gr.HTML(), gr.HTML(), gr.HTML()])
+        report_tab = gr.Interface(fn=analyze_report, inputs=gr.File(label="Upload Patient Report (PDF)", file_count="single"), outputs=gr.Textbox(label="Report Summary", interactive=False))
+
+        gr.TabbedInterface([xray_tab, report_tab], tab_names=["X-ray Analysis", "Report Analysis"]).launch(share=True)
+
+demo = create_interface()