Update app.py

app.py

@@ -7,43 +7,33 @@ from reportlab.lib.pagesizes import letter
 from reportlab.pdfgen import canvas
 import os
 
-# Load the trained model
+# Load the trained model once
 model = tf.keras.models.load_model("my_keras_model.h5")
-
-# Define image size based on the model's input requirement
-image_size = (224, 224)
+image_size = (224, 224)  # Ensure consistent image size
 
 # Function to analyze injury severity
 def analyze_injury(prediction):
     if prediction < 0.3:
-        severity = "Mild"
-        treatment = "Rest, pain relievers, and follow-up X-ray."
-        gov_cost = "₹2,000 - ₹5,000"
-        private_cost = "₹10,000 - ₹20,000"
+        return "Mild", "Rest and pain relief.", "₹2,000 - ₹5,000", "₹10,000 - ₹20,000"
     elif 0.3 <= prediction < 0.7:
-        severity = "Moderate"
-        treatment = "Plaster cast or splint; possible minor surgery."
-        gov_cost = "₹8,000 - ₹15,000"
-        private_cost = "₹30,000 - ₹60,000"
+        return "Moderate", "Plaster cast or minor surgery.", "₹8,000 - ₹15,000", "₹30,000 - ₹60,000"
     else:
-        severity = "Severe"
-        treatment = "Major surgery with metal implants, extensive physiotherapy."
-        gov_cost = "₹20,000 - ₹50,000"
-        private_cost = "₹1,00,000+"
-    
-    return severity, treatment, gov_cost, private_cost
+        return "Severe", "Major surgery with metal implants.", "₹20,000 - ₹50,000", "₹1,00,000+"
 
 # Function to generate report
 def generate_report(patient_name, age, gender, xray1_path, xray2_path):
+    if not os.path.exists(xray1_path) or not os.path.exists(xray2_path):
+        return "Error: One or both X-ray images are missing!"
+
     try:
         # Process X-ray 1
-        img1 = Image.open(xray1_path).resize(image_size)
+        img1 = Image.open(xray1_path).resize(image_size).convert("RGB")
         img_array1 = image.img_to_array(img1)
         img_array1 = np.expand_dims(img_array1, axis=0) / 255.0  
         prediction1 = model.predict(img_array1)[0][0]
 
         # Process X-ray 2
-        img2 = Image.open(xray2_path).resize(image_size)
+        img2 = Image.open(xray2_path).resize(image_size).convert("RGB")
         img_array2 = image.img_to_array(img2)
         img_array2 = np.expand_dims(img_array2, axis=0) / 255.0  
         prediction2 = model.predict(img_array2)[0][0]
@@ -65,10 +55,12 @@ def generate_report(patient_name, age, gender, xray1_path, xray2_path):
         c.drawString(100, 650, f"Recommended Treatment: {treatment}")
         c.drawString(100, 630, f"Estimated Cost (Govt Hospital): {gov_cost}")
         c.drawString(100, 610, f"Estimated Cost (Private Hospital): {private_cost}")
-        
         c.save()
 
-        return report_path
+        if os.path.exists(report_path):
+            return report_path
+        else:
+            return "Error: Report generation failed!"
 
     except Exception as e:
         return f"Error generating report: {str(e)}"
@@ -88,6 +80,5 @@ interface = gr.Interface(
     description="Enter patient details, upload two X-ray images, and generate a detailed medical report with treatment suggestions and cost estimates."
 )
 
-# Launch the Gradio app
 if __name__ == "__main__":
-    interface.launch()
+    interface