Update app.py

app.py

@@ -5,12 +5,13 @@ 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", hf_token=HF_TOKEN)
+client = Client("mangoesai/Elections_Comparison_Agent_V4.1", hf_token=HF_TOKEN)
 
 # client_name = ['2016 Election','2024 Election', 'Comparison two years']
 
@@ -79,22 +80,136 @@ def heatmap(top_n):
     
     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))
+    return result
+
+
+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.Row():        
         with gr.Column():
-            with gr.Row():
-                top_n = gr.Dropdown(choices=[1,2,3,4,5,6,7,8,9,10])
-            with gr.Row():
-                fresh_btn = gr.Button("Refresh Heatmap")
+            top_n = gr.Dropdown(choices=[1,2,3,4,5,6,7,8,9,10])
+            table_btn = gr.Button("Overall pivot table")
+            show_pivot_table = gr.Dataframe(headers=['Index'] + list(df.columns))
+
         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.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")
@@ -125,7 +240,8 @@ with gr.Blocks(title="Reddit Election Analysis") as demo:
                 label="Response",
                 lines=20
             )
-
+    
+    gr.Markdown("## Top works of the relevant Q&A")
     with gr.Row():
         output_plot = gr.Plot(
             label="Topic Distribution",
@@ -148,24 +264,38 @@ with gr.Blocks(title="Reddit Election Analysis") as demo:
             }
         </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]
+    )
 
+    table_btn.click(
+        fn=get_heatmap_pivot_table,
+        inputs= top_n,
+        outputs=show_pivot_table
+    )
     # Update both outputs when submit is clicked
-    # submit_btn.click(
-    #     fn=stream_chat_with_rag,
-    #     inputs=[query_input, year_selector],
-    #     outputs=[output_text, output_plot]
-    # )
     submit_btn.click(
-        fn=stream_chat_with_rag,
+        fn=process_query,
         inputs=[query_input, year_selector],
         outputs=output_text
     )
 
 
+if __name__ == "__main__":
+    demo.launch(share=True)
+
+
 if __name__ == "__main__":
     demo.launch(share=True)