Update app.py

app.py

@@ -2,6 +2,7 @@ import gradio as gr
 from PIL import Image
 import torch
 from torchvision import models, transforms
+import PyPDF2
 import logging
 import os
 
@@ -52,7 +53,7 @@ def preprocess_image(image):
 def predict_xray(image):
     try:
         if image is None:
-            return "Error: No image uploaded.", ""
+            return "Error: No image uploaded.", "", ""
 
         image_tensor = preprocess_image(image)
         with torch.no_grad():
@@ -60,19 +61,61 @@ def predict_xray(image):
         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))}
 
-        # Get the most likely condition
+        # 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]
+        
+        # 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 only the diagnosis summary
-        summary = f"**Diagnosis**: The most likely diagnosis is: <b>{most_likely_condition}</b> with a confidence of <b>{confidence:.2f}%</b>."
+        # 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}%")
-        return summary
+        return summary, detailed_results, additional_feedback
 
     except Exception as e:
         logger.error(f"Error in predict_xray: {str(e)}")
-        return f"Error: {str(e)}", ""
+        return f"Error: {str(e)}", "", ""
+
+# Define function to read and analyze patient reports (PDFs)
+def analyze_report(file):
+    text = ""
+    if file and file.name.endswith(".pdf"):
+        try:
+            pdf_reader = PyPDF2.PdfReader(file)
+            for page in pdf_reader.pages:
+                text += page.extract_text() or ""
+            report_summary = f"Patient Report (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
 def create_interface():
@@ -83,6 +126,9 @@ def create_interface():
         .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>")
@@ -91,18 +137,28 @@ def create_interface():
         with gr.Row():
             with gr.Column(scale=1):
                 xray_input = gr.Image(label="Upload X-ray", type="pil", elem_id="xray-input")
+            with gr.Column(scale=1):
+                report_input = gr.File(label="Upload Patient Report (PDF)", file_count="single", elem_id="report-input")
         
         with gr.Row():
             predict_button = gr.Button("Analyze X-ray", elem_classes="gradio-button")
+            report_button = gr.Button("Analyze Report", elem_classes="gradio-button")
         
         with gr.Column():
-            xray_output = gr.HTML(label="Diagnosis Summary", elem_classes="result-box")
+            xray_output = gr.HTML(label="X-ray Diagnosis Summary", elem_classes="result-box")
+            xray_result = gr.HTML(label="Detailed X-ray Results", elem_classes="result-box")
+            additional_feedback = gr.HTML(label="Additional Feedback", elem_classes="result-box")
+            report_output = gr.Textbox(label="Report Summary", interactive=False, elem_classes="result-box")
 
         predict_button.click(
             fn=predict_xray,
             inputs=xray_input,
-            outputs=xray_output
+            outputs=[xray_output, xray_result, additional_feedback]
         )
+        report_button.click(
+            fn=analyze_report,
+            inputs=report_input,
+            outputs=report_output)
     
     return demo