Update app.py

app.py

@@ -12,6 +12,10 @@ from reportlab.pdfgen import canvas
 # Load the trained model
 model = tf.keras.models.load_model("my_keras_model.h5")
 
+# Read HTML content from `re.html`
+with open("templates/re.html", "r", encoding="utf-8") as file:
+    html_content = file.read()
+
 # Function to process X-rays and generate a PDF report
 def generate_report(name, age, gender, xray1, xray2):
     image_size = (224, 224)
@@ -56,20 +60,28 @@ def generate_report(name, age, gender, xray1, xray2):
     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="filepath", label="Upload X-ray Image 1"),
-        gr.Image(type="filepath", 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."
-)
+with gr.Blocks() as app:
+    gr.HTML(html_content)  # Display `re.html` content in Gradio
+    gr.Markdown("## Bone Fracture Detection System")
+    
+    with gr.Row():
+        name = gr.Textbox(label="Patient Name")
+        age = gr.Number(label="Age")
+        gender = gr.Radio(["Male", "Female", "Other"], label="Gender")
+
+    with gr.Row():
+        xray1 = gr.Image(type="filepath", label="Upload X-ray Image 1")
+        xray2 = gr.Image(type="filepath", label="Upload X-ray Image 2")
+
+    submit_button = gr.Button("Generate Report")
+    output_file = gr.File(label="Download Report")
+
+    submit_button.click(
+        generate_report,
+        inputs=[name, age, gender, xray1, xray2],
+        outputs=[output_file],
+    )
 
 # Launch the Gradio app
 if __name__ == "__main__":
-    interface.launch()
+    app.launch()