Update app.py

app.py

@@ -1,6 +1,4 @@
 import os
-#os.environ["CUDA_VISIBLE_DEVICES"] = "-1"  # Force TensorFlow to use CPU
-
 import gradio as gr
 import tensorflow as tf
 import numpy as np
@@ -63,13 +61,43 @@ def predict_text(image):
 
 
 # Gradio UI
-interface = gr.Interface(
-    fn=predict_text,
-    inputs=gr.Image(type="pil"),
-    outputs="text",
-    title="Handwritten Prescription OCR",
-    description="Upload a prescription image, and the model will return the recognized text."
-)
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    
+    # Header
+    gr.Markdown(
+        """
+        <h1 style="text-align: center; color: #2F4F4F;">🏥 Prescription Recognition AI</h1>
+        <p style="text-align: center; font-size: 18px;">Upload or take a picture of a handwritten prescription. The AI will identify the drug name.</p>
+        <hr>
+        """,
+        elem_id="header"
+    )
+
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown("### 📸 Upload or Capture an Image")
+            image_input = gr.Image(type="pil", tool="editor", label="Upload or Capture Prescription")
+        
+        with gr.Column():
+            gr.Markdown("### 💊 Recognized Drug Name")
+            output_text = gr.Textbox(label="Predicted Drug Name", interactive=False)
+
+    # Process button
+    with gr.Row():
+        submit_button = gr.Button("🔍 Identify Prescription", variant="primary")
+    
+    # On button click, predict the drug name
+    submit_button.click(fn=predict_text, inputs=image_input, outputs=output_text)
+
+    # Footer
+    gr.Markdown(
+        """
+        <hr>
+        <p style="text-align: center;">Developed by <strong>Your Name</strong> | Powered by AI & Gradio</p>
+        """,
+        elem_id="footer"
+    )
+
 
 if __name__ == "__main__":
     interface.launch()