Update app.py

app.py

@@ -1,58 +1,75 @@
 import gradio as gr
 import pandas as pd
+import os
 
-# Load the dataset
-# Ensure 'userdataset.csv' is uploaded in the same directory in Hugging Face Spaces
+# Function to load the dataset
 def load_data():
-    dataset = pd.read_excel('chapter1_english_9th.xlsx', sheet_name=None)
+    # 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:
+        raise FileNotFoundError("Dataset file not found! Please upload the 'chapter1_english_9th.xlsx' file.")
     return dataset
 
 # Load the dataset
 dataset = load_data()
 
-# Function to retrieve content based on selections
+# Function to retrieve content based on exact selections
 def get_content(selected_class, selected_subject, selected_chapter, content_type):
-    sheet_mapping = {
-        'Short Questions': 'Sheet1',
-        'Synonyms and Urdu Meaning': 'Sheet2',
-        'Grammar Identification': 'Sheet3'
-    }
-    sheet_name = sheet_mapping.get(content_type)
-    data = dataset[sheet_name]
-    # Filter data here if necessary (e.g., based on selected class, subject, chapter)
-    return data.to_html(index=False)  # Return HTML table
-
-# Function to update content based on inputs
-def update_content(selected_class, selected_subject, selected_chapter, selected_content_type):
-    content = get_content(selected_class, selected_subject, selected_chapter, selected_content_type)
-    html_output = f"""
-    <h3>{selected_content_type} for {selected_class} - {selected_subject} - {selected_chapter}</h3>
-    {content}
-    """
-    return html_output
-
-# Define the Gradio app layout and interaction
-with gr.Blocks() as app:
-    gr.Markdown("# Educational Content Access App")
+    # 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',
+            'Synonyms and Urdu Meaning': 'Sheet2',
+            'Grammar Identification': 'Sheet3'
+        }
+    elif selected_class == 'Class 10' and selected_subject == 'English' and selected_chapter == 'Chapter 2':
+        sheet_mapping = {
+            'Short Questions': 'Sheet4',
+            'Synonyms and Urdu Meaning': 'Sheet5',
+            'Grammar Identification': 'Sheet6'
+        }
+    else:
+        return None  # No data available for other combinations
     
-    # Dropdowns for user selection
-    selected_class = gr.Dropdown(choices=['Class 9', 'Class 10', 'Class 11', 'Class 12'], label="Select Class")
-    selected_subject = gr.Dropdown(choices=['Computer', 'English', 'Physics', 'Chemistry', 'Math'], label="Select Subject")
-    selected_chapter = gr.Dropdown(choices=[f'Chapter {i}' for i in range(1, 13)], label="Select Chapter")
-    selected_content_type = gr.Dropdown(choices=['Short Questions', 'Synonyms and Urdu Meaning', 'Grammar Identification'], label="Select Content Type")
-    
-    # Button to display content
-    display_button = gr.Button("Show Content")
+    sheet_name = sheet_mapping.get(content_type)
+
+    # Ensure the sheet exists in the dataset
+    if sheet_name in dataset:
+        return dataset[sheet_name]
     
-    # HTML output area for displaying content
-    content_output = gr.HTML()
-
-    # Update content based on button click and user selections
-    display_button.click(
-        update_content,
-        inputs=[selected_class, selected_subject, selected_chapter, selected_content_type],
-        outputs=content_output
+    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.
+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
+def gradio_interface(selected_class, selected_subject, selected_chapter, content_type):
+    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 "No data available for the selected options."
+
+# Define the Gradio interface
+def run_app():
+    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="dataframe"  # This will display the data as a table
     )
+    interface.launch()
 
 # Run the app
-app.launch()
+run_app()