Update app.py

app.py

@@ -57,10 +57,6 @@ transform = transforms.Compose([
     transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
 ])
 
-# Helper to sanitize filename
-def clean_filename(name):
-    return "".join(c for c in name if c.isalnum() or c in (' ', '_', '-')).rstrip()
-
 # Prediction and PDF generation function
 def predict(image, patient_name, xray_date):
     if image is None:
@@ -98,7 +94,7 @@ def predict(image, patient_name, xray_date):
     else:
         comment = f"🔎 Possible {top_label}, but confidence is low."
 
-    # Generate PDF
+    # Generate PDF report
     pdf = FPDF()
     pdf.add_page()
     pdf.set_font("Arial", 'B', 16)
@@ -108,8 +104,8 @@ def predict(image, patient_name, xray_date):
     # Patient Details
     pdf.set_font("Arial", '', 12)
     pdf.cell(0, 10, f"Patient Name: {patient_name}", ln=True)
-    pdf.cell(0, 10, f"Date of X-ray: {xray_date}", ln=True)
-    pdf.cell(0, 10, f"Report Generated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}", ln=True)
+    pdf.cell(0, 10, f"X-ray Date: {xray_date}", ln=True)
+    pdf.cell(0, 10, f"Report Date: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}", ln=True)
     pdf.ln(10)
 
     # Top 5 Predictions
@@ -128,12 +124,11 @@ def predict(image, patient_name, xray_date):
     pdf.set_font("Arial", '', 12)
     pdf.multi_cell(0, 10, comment)
 
-    # Save PDF with clean filename
-    safe_name = clean_filename(patient_name.replace(" ", "_"))
-    report_filename = f"{safe_name}_{xray_date}_Report.pdf"
-    pdf.output(report_filename)
+    # Save PDF
+    output_pdf_path = "chest_xray_report.pdf"
+    pdf.output(output_pdf_path)
 
-    return report_filename
+    return output_pdf_path
 
 # Gradio Interface
 interface = gr.Interface(