Update app.py

app.py

@@ -1,11 +1,73 @@
 import gradio as gr
-import subprocess
+import tensorflow as tf
+import numpy as np
+from tensorflow.keras.preprocessing import image
+from PIL import Image
+from reportlab.lib.pagesizes import letter
+from reportlab.pdfgen import canvas
+import os
 
-# Start Flask in the background
-subprocess.Popen(["python3", "flask_app.py"])
+# Load the trained model
+model = tf.keras.models.load_model("my_keras_model.h5")
 
-def generate_report():
-    return "Gradio App Running!"
+# Function to process X-rays and generate a PDF report
+def generate_report(name, age, gender, xray1, xray2):
+    image_size = (224, 224)
 
-interface = gr.Interface(fn=generate_report, inputs=[], outputs="text")
-interface.launch(server_name="0.0.0.0", server_port=7861)  # Run Gradio on 7861
+    def predict_fracture(xray):
+        img = Image.open(xray).resize(image_size)
+        img_array = image.img_to_array(img) / 255.0
+        img_array = np.expand_dims(img_array, axis=0)
+        prediction = model.predict(img_array)[0][0]
+        return prediction
+
+    # Predict on both X-rays
+    prediction1 = predict_fracture(xray1)
+    prediction2 = predict_fracture(xray2)
+    avg_prediction = (prediction1 + prediction2) / 2
+    diagnosed_class = "Fractured" if avg_prediction > 0.5 else "Normal"
+
+    # Injury severity classification
+    severity = "Mild" if avg_prediction < 0.3 else "Moderate" if avg_prediction < 0.7 else "Severe"
+    treatment = {
+        "Mild": "Rest, pain relievers, follow-up X-ray.",
+        "Moderate": "Plaster cast, possible minor surgery.",
+        "Severe": "Major surgery, metal implants, physiotherapy."
+    }[severity]
+    gov_cost = {"Mild": "₹2,000 - ₹5,000", "Moderate": "₹8,000 - ₹15,000", "Severe": "₹20,000 - ₹50,000"}[severity]
+    private_cost = {"Mild": "₹10,000 - ₹20,000", "Moderate": "₹30,000 - ₹60,000", "Severe": "₹1,00,000+"}[severity]
+
+    # Generate PDF report
+    report_path = f"{name}_fracture_report.pdf"
+    c = canvas.Canvas(report_path, pagesize=letter)
+    c.setFont("Helvetica", 12)
+    c.drawString(100, 750, f"Patient Name: {name}")
+    c.drawString(100, 730, f"Age: {age}")
+    c.drawString(100, 710, f"Gender: {gender}")
+    c.drawString(100, 690, f"Diagnosis: {diagnosed_class}")
+    c.drawString(100, 670, f"Injury Severity: {severity}")
+    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  # Return path for auto-download
+
+# Define Gradio Interface
+interface = gr.Interface(
+    fn=generate_report,
+    inputs=[
+        gr.Textbox(label="Patient Name"),
+        gr.Number(label="Age"),
+        gr.Radio(["Male", "Female", "Other"], label="Gender"),
+        gr.Image(type="file", label="Upload X-ray Image 1"),
+        gr.Image(type="file", label="Upload X-ray Image 2"),
+    ],
+    outputs=gr.File(label="Download Report"),
+    title="Bone Fracture Detection & Medical Report",
+    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()