import gradio as gr
import pandas as pd
import os

# Function to load the dataset
def load_data():
    if os.path.exists('chapter1_english_9th.xlsx'):
        dataset = pd.read_excel('chapter1_english_9th.xlsx', sheet_name=None)
        print(f"Dataset loaded successfully. Sheets available: {dataset.keys()}")  # Debugging line
    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 exact selections
def get_content(selected_class, selected_subject, selected_chapter, content_type):
    print(f"Selected Class: {selected_class}, Subject: {selected_subject}, Chapter: {selected_chapter}, Content Type: {content_type}")  # Debugging line

    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:
        print("No matching data for the given selection.")  # Debugging line
        return None
    
    sheet_name = sheet_mapping.get(content_type)

    # Check if sheet name exists
    if sheet_name in dataset:
        print(f"Found data for {sheet_name}")  # Debugging line
        return dataset[sheet_name]
    else:
        print(f"Sheet '{sheet_name}' not found in dataset.")  # Debugging line
        return None

# Groq Model Processing (Placeholder function)
def groq_process_data(content):
    return content.head()  # Returning first 5 rows for illustration

# Gradio interface function for new result page
def result_page(content):
    return content

# 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:
        processed_content = groq_process_data(content)
        # Launching a new page with the result
        gr.Interface(fn=result_page, inputs=gr.Dataframe(value=processed_content), outputs=gr.Dataframe()).launch()
        return processed_content
    else:
        return "No data available currently."

# Define the Gradio interface with attractive layout and fresh output page
def run_app():
    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")

        # Connecting the button to the output, results will be shown on a new page
        submit_button.click(gradio_interface, inputs=[selected_class, selected_subject, selected_chapter, content_type], outputs=[])

    demo.launch(share=True)  # Launch the app with the share parameter to open it on a new page

# Run the app
run_app()