app.py

import gradio as gr
from gradio_client import Client, handle_file
import seaborn as sns
import matplotlib.pyplot as plt
import os
import pandas as pd
from io import StringIO
from linePlot import plot_stacked_time_series, plot_emotion_topic_grid

# Define your Hugging Face token (make sure to set it as an environment variable)
HF_TOKEN = os.getenv("HF_TOKEN")  # Replace with your actual token if not using an environment variable

# Initialize the Gradio Client for the specified API
client = Client("mangoesai/Elections_Comparison_Agent_V4.1", hf_token=HF_TOKEN)

# client_name = ['2016 Election','2024 Election', 'Comparison two years']



def stream_chat_with_rag(
    message: str,
    # history: list,
    client_name: str
):
    # print(f"Message: {message}")
    #answer = client.predict(question=question, api_name="/run_graph")
    answer, fig = client.predict(
    	query= message,
		election_year=client_name,
		api_name="/process_query"
    )

    # Debugging: Print the raw response
    print("Raw answer from API:")
    print(answer)
    print("top works from API:")
    print(fig)

    # return answer, fig
    return answer


    

def heatmap(top_n):
    # df = pd.read_csv('submission_emotiontopics2024GPTresult.csv')
    # topics_df = gr.Dataframe(value=df, label="Data Input")
    pivot_table = client.predict(
    	top_n= top_n,
		api_name="/get_heatmap_pivot_table"
    )
    print(pivot_table)
    print(type(pivot_table)) 
    """
    pivot_table is a dict like:
    {'headers': ['Index', 'economy', 'human rights', 'immigrant', 'politics'], 
    'data': [['anger', 55880.0, 557679.0, 147766.0, 180094.0], 
             ['disgust', 26911.0, 123112.0, 64567.0, 46460.0], 
             ['fear', 51466.0, 188898.0, 113174.0, 150578.0], 
             ['neutral', 77005.0, 192945.0, 20549.0, 190793.0]], 
    'metadata': None}
    """
    

    # transfere dictionary to df
    df = pd.DataFrame(pivot_table['data'], columns=pivot_table['headers'])
    df.set_index('Index', inplace=True)
    
    plt.figure(figsize=(10, 8))
    sns.heatmap(df,
                cmap='YlOrRd',
                cbar_kws={'label': 'Weighted Frequency'},
                square=True)
    
    plt.title(f'Top {top_n} Emotions vs Topics Weighted Frequency')
    plt.xlabel('Topics')
    plt.ylabel('Emotions')
    plt.xticks(rotation=45, ha='right')
    plt.tight_layout()
    
    return plt.gcf()

def linePlot_time_series(viz_type, weight, top_n):
    result = client.predict(
    		viz_type=viz_type,
    		weight=weight,
    		top_n=top_n,
    		api_name="/linePlot_time_series"
    )
    
    print("============== timeseries df transfer from pivate to public ===============")
    print(result)
    print(type(result))

    df = pd.DataFrame(result['data'], columns=result['headers'])
    df.set_index('Index', inplace=True)
    return df


def update_visualization(viz_type, weight, top_n):
    """
    Update visualization based on user inputs and selected visualization type
    
    Parameters:
    -----------
    viz_type : str
        Type of visualization to show ('emotions', 'topics', or 'grid')
    weight : float
        Weight for scoring (0-1)
    top_n : int
        Number of top items to show
    """
    try:

        #     return None, "Error: Start date must be before end date"
        series = linePlot_time_series(viz_type, weight, top_n)
        if viz_type == "emotions":
            # Create emotion time series
            # series = linePlot_time_series(viz_type, weight, top_n)
            fig = plot_stacked_time_series(
                series,
                f'Top {top_n} Emotions Popularity'
            )
            message = "Emotion time series updated"
            
        elif viz_type == "topics":
            # Create topic time series
            # series = linePlot_time_series(viz_type, weight, top_n)
            fig = plot_stacked_time_series(
                series,
                f'Top {top_n} Topics Popularity'
            )
            message = "Topic time series updated"
            
        else:  # viz_type == "grid"
            # Create emotion-topic grid
            # pair_series = linePlot_time_series(viz_type, weight, top_n)
            fig = plot_emotion_topic_grid(series, top_n)
            message = "Emotion-Topic grid updated"
        
        return fig, message
        
    except Exception as e:
        return None, f"Error: {str(e)}"
        





# Create Gradio interface
with gr.Blocks(title="Reddit Election Analysis") as demo:
    gr.Markdown("# Reddit Public sentiment & Social topic distribution ")
    with gr.Row():        
        with gr.Column():
            top_n = gr.Dropdown(choices=[1,2,3,4,5,6,7,8,9,10])
            fresh_btn = gr.Button("Refresh Heatmap")

        with gr.Column():
            
            # with gr.Row():
            output_heatmap = gr.Plot(
                label="Top Public sentiment & Social topic Heatmap",
                container=True,  # Ensures the plot is contained within its area
                elem_classes="heatmap-plot"  # Add a custom class for styling
            )
    gr.Markdown("# Get the time series of the Public sentiment & Social topic")
    with gr.Row():
        with gr.Column(scale=1):
            # Control panel
            lineGraph_type = gr.Dropdown(choices = ['emotions', 'topics', '2Dmatrix'])
                            
            weight_slider = gr.Slider(
                minimum=0,
                maximum=1,
                value=0.5,
                step=0.1,
                label="Weight (Score vs. Frequency)"
            )
            
            top_n_slider = gr.Slider(
                minimum=2,
                maximum=10,
                value=5,
                step=1,
                label="Top N Items"
            )

            # start_date_picker = gr.Date(
            #     value=date_min.date(),
            #     label="Start Date",
            #     info=f"Available from: {date_min.strftime('%Y-%m-%d')}"
            # )
            
            # end_date_picker = gr.Date(
            #     value=date_max.date(),
            #     label="End Date",
            #     info=f"Available until: {date_min.strftime('%Y-%m-%d')}"
            # )
        

        # with gr.Column():
            viz_dropdown = gr.Dropdown(
                choices=["emotions", "topics", "grid"],
                value="emotions",
                label="Visualization Type",
                info="Select the type of visualization to display"
            )
            linePlot_btn = gr.Button("Update Visualizations")
            linePlot_status_text = gr.Textbox(label="Status", interactive=False)
        
        with gr.Column(scale=3):
            time_series_fig = gr.Plot()

    gr.Markdown("# Reddit Election Posts/Comments Analysis")
    gr.Markdown("Ask questions about election-related comments and posts")

    with gr.Row():
        with gr.Column():
            year_selector = gr.Radio(
                choices=["2016 Election", "2024 Election", "Comparison two years"],
                label="Select Election Year",
                value="2016 Election"
            )

            query_input = gr.Textbox(
                label="Your Question",
                placeholder="Ask about election comments or posts..."
            )

            submit_btn = gr.Button("Submit")

            gr.Markdown("""
            ## Example Questions:
            - Is there any comments don't like the election results
            - Summarize the main discussions about voting process
            - What are the common opinions about candidates?
            """)
        with gr.Column():
            output_text = gr.Textbox(
                label="Response",
                lines=20
            )
    
    gr.Markdown("## Top works of the relevant Q&A")
    with gr.Row():
        output_plot = gr.Plot(
            label="Topic Distribution",
            container=True,  # Ensures the plot is contained within its area
            elem_classes="topic-plot"  # Add a custom class for styling
        )

    # Add custom CSS to ensure proper plot sizing
    gr.HTML("""
        <style>
            .topic-plot {
                min-height: 600px;
                width: 100%;
                margin: auto;
            }
            .heatmap-plot {
                min-height: 400px;
                width: 100%;
                margin: auto;
            }
        </style>
    """)
    # topics_df = gr.Dataframe(value=df, label="Data Input")


    
    fresh_btn.click(
        fn=heatmap,
        inputs=top_n,
        outputs=output_heatmap
    )
    
    linePlot_btn.click(
        fn = update_visualization,
        inputs = [viz_dropdown,weight_slider,top_n_slider],
        outputs = [time_series_fig, linePlot_status_text]
    )

    # Update both outputs when submit is clicked
    submit_btn.click(
        fn=stream_chat_with_rag,
        inputs=[query_input, year_selector],
        outputs=output_text
    )


if __name__ == "__main__":
    demo.launch(share=True)