Update app.py

app.py

@@ -4,7 +4,6 @@ import os
 
 # Function to load the dataset
 def load_data():
-    # Ensure the dataset is uploaded to Hugging Face or placed in the correct directory
     if os.path.exists('chapter1_english_9th.xlsx'):
         dataset = pd.read_excel('chapter1_english_9th.xlsx', sheet_name=None)
     else:
@@ -16,7 +15,6 @@ dataset = load_data()
 
 # Function to retrieve content based on exact selections
 def get_content(selected_class, selected_subject, selected_chapter, content_type):
-    # Map combinations of selections to sheet names
     if selected_class == 'Class 9' and selected_subject == 'English' and selected_chapter == 'Chapter 1':
         sheet_mapping = {
             'Short Questions': 'Sheet1',
@@ -30,21 +28,17 @@ def get_content(selected_class, selected_subject, selected_chapter, content_type
             'Grammar Identification': 'Sheet6'
         }
     else:
-        return None  # No data available for other combinations
+        return None
     
     sheet_name = sheet_mapping.get(content_type)
 
-    # Ensure the sheet exists in the dataset
     if sheet_name in dataset:
         return dataset[sheet_name]
     
     return None
 
-# Groq Model Processing (Assumed example function for data processing using Groq)
-# This is just a placeholder function. Replace it with actual Groq processing logic.
+# Groq Model Processing (Placeholder function)
 def groq_process_data(content):
-    # Example Groq processing
-    # Assuming that Groq operates on this content in some form, replace with real Groq API logic
     return content.head()  # Returning first 5 rows for illustration
 
 # Gradio interface function
@@ -52,30 +46,38 @@ def gradio_interface(selected_class, selected_subject, selected_chapter, content
     content = get_content(selected_class, selected_subject, selected_chapter, content_type)
     
     if content is not None and not content.empty:
-        # Process the content using Groq (or simulate processing)
         processed_content = groq_process_data(content)
         return processed_content
     else:
-        # Return message when no data is found
         return "No data available currently."
 
-# Define the Gradio interface
+# Define the Gradio interface with attractive layout and fresh output page
 def run_app():
-    # Set up the Gradio interface
-    interface = gr.Interface(
-        fn=gradio_interface,
-        inputs=[
-            gr.Dropdown(label="Select Class", choices=["Class 9", "Class 10", "Class 11", "Class 12"]),
-            gr.Dropdown(label="Select Subject", choices=["Computer", "English", "Physics", "Chemistry", "Math"]),
-            gr.Dropdown(label="Select Chapter", choices=[f"Chapter {i}" for i in range(1, 13)]),
-            gr.Dropdown(label="Select Content Type", choices=["Short Questions", "Synonyms and Urdu Meaning", "Grammar Identification"])
-        ],
-        outputs="text",  # Display result as text (to show message or processed content)
-        live=False,  # Disable live updates
-        title="Educational Content Access App",
-        description="Select the class, subject, chapter, and content type to view educational content. If no data is available for the selected options, a message will be displayed."
-    )
-    interface.launch()
+    with gr.Blocks() as demo:
+        # Header section
+        gr.Markdown("# Educational Content Access App")
+        gr.Markdown("### Select options to view educational content.")
+
+        # Input section: Using card-style layout for a neat appearance
+        with gr.Row():
+            with gr.Column(scale=1):
+                selected_class = gr.Dropdown(label="Select Class", choices=["Class 9", "Class 10", "Class 11", "Class 12"], interactive=True)
+                selected_subject = gr.Dropdown(label="Select Subject", choices=["Computer", "English", "Physics", "Chemistry", "Math"], interactive=True)
+                selected_chapter = gr.Dropdown(label="Select Chapter", choices=[f"Chapter {i}" for i in range(1, 13)], interactive=True)
+                content_type = gr.Dropdown(label="Select Content Type", choices=["Short Questions", "Synonyms and Urdu Meaning", "Grammar Identification"], interactive=True)
+
+        # Submit Button to generate content
+        submit_button = gr.Button("Show Content", variant="primary")
+
+        # Output Section: This will display the result on a new page when content is found
+        output_area = gr.TabbedInterface([
+            gr.Tab("Results", gr.Dataframe()),
+            gr.Tab("No Data Available", gr.Textbox())
+        ])
+        
+        submit_button.click(gradio_interface, inputs=[selected_class, selected_subject, selected_chapter, content_type], outputs=[output_area])
+
+    demo.launch()
 
 # Run the app
 run_app()