Update app.py

app.py

@@ -36,9 +36,15 @@ def stream_chat_with_rag(
     print(answer)
     print("top works from API:")
     print(fig)
-
+    
+    plot_bytes = base64.b64decode(fig['plot'].split(',')[1])
+    img = plt.imread(BytesIO(fig), format='PNG')
+    plt.figure(dpi = 150)
+    plt.imshow(img)
+    plt.axis('off')
+    plt.show()
     # return answer, fig
-    return answer
+    return answe, plt.gcf()
 
 
     
@@ -81,79 +87,16 @@ 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))
-
-#     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, top_n):
-    plot_bytes = base64.b64decode(plot_base64['plot'].split(',')[1])
-    img = plt.imread(BytesIO(plot_bytes), format='PNG')
-    plt.figure(figsize = (12, 2*top_n), dpi = 150)
-    plt.imshow(img)
-    plt.axis('off')
-    plt.show()
-    return plt.gcf()
+# def decode_plot(plot_base64, top_n):
+#     plot_bytes = base64.b64decode(plot_base64['plot'].split(',')[1])
+#     img = plt.imread(BytesIO(plot_bytes), format='PNG')
+#     plt.figure(figsize = (12, 2*top_n), dpi = 150)
+#     plt.imshow(img)
+#     plt.axis('off')
+#     plt.show()
+#     return plt.gcf()
 
 
 def linePlot(viz_type, weight, top_n):
@@ -165,8 +108,16 @@ def linePlot(viz_type, weight, 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],top_n), result[1]
+    # result is a tuble of dictionary of (plot_base64, str), string message of description of the plot
+    plot_base64 = result[0]
+
+    plot_bytes = base64.b64decode(plot_base64['plot'].split(',')[1])
+    img = plt.imread(BytesIO(plot_bytes), format='PNG')
+    plt.figure(figsize = (12, 2*top_n), dpi = 150)
+    plt.imshow(img)
+    plt.axis('off')
+    plt.show()   
+    return plt.gcf(), result[1]