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, BytesIO
import base64
# 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)}"



def decode_plot(plot_base64):
    plot_bytes = base64.b64decode(plot_base64['plot'].split(',')[1])
    img = plt.imread(BytesIO(plot_bytes), format='PNG')
    plt.imshow(img)
    plt.axis('off')
    plt.show()
    return plt.gcf()


def linePlot(viz_type, weight, top_n):
    # client = Client("mangoesai/Elections_Comparison_Agent_V4.1")
    result = client.predict(
    		viz_type=viz_type,
    		weight=weight,
    		top_n=top_n,
    		api_name="/linePlot_3C1"
    )
    # print(result)
    # result is a tuble of dictionary of plot_base64, and a string message of description of the plot
    return decode_plot(result[0])

    

# 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"
            )


        # 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 = linePlot,
        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)