Update app.py

app.py

@@ -7,7 +7,7 @@ import pandas as pd
 from io import StringIO, BytesIO
 import base64
 import json
-import plotly.io as pio
+# import plotly.io as pio
 # 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)
@@ -36,24 +36,42 @@ def stream_chat_with_rag(
     # Debugging: Print the raw response
     print("Raw answer from API:")
     print(answer)
-    print("top works from API:")
-    print(fig)
+    # print("top works from API:")
+    # print(fig)
     
-    fig_dict = json.loads(plotly_data['plot'])
+    # fig_dict = json.loads(plotly_data['plot'])
     
-    # Render the figure
-    fig = pio.from_json(json.dumps(fig_dict))
-    fig.show() 
+    # # Render the figure
+    # fig = pio.from_json(json.dumps(fig_dict))
+    # fig.show() 
     return answer, fig
 
 
+def predict(message, history):
+    history_langchain_format = []
+    for msg in history:
+        if msg['role'] == "user":
+            history_langchain_format.append(HumanMessage(content=msg['content']))
+        elif msg['role'] == "assistant":
+            history_langchain_format.append(AIMessage(content=msg['content']))
+    history_langchain_format.append(HumanMessage(content=message))
+    gpt_response = llm(history_langchain_format)
+    return gpt_response.content
     
-def chat_function(chat_history, year, user_message):
-    # Mock response (replace this with your logic for generating a response)
-    response = f"Year selected: {year}. Here's a response."
-    answer = stream_chat_with_rag(user_message, year)[0]
-    chat_history.append((user_message, response +"\n"+ answer))
-    return chat_history, ""
+def chat_function(message, history, year):
+    history_langchain_format = []
+    for msg in history:
+        if msg['role'] == "user":
+            history_langchain_format.append(HumanMessage(content=msg['content']))
+        elif msg['role'] == "assistant":
+            history_langchain_format.append(AIMessage(content=msg['content']))
+    history_langchain_format.append(HumanMessage(content=message))
+    rag_response = stream_chat_with_rag(history_langchain_format,year)[0]
+    
+    # response = f"Year selected: {year}. Here's a response."
+    # answer = stream_chat_with_rag(user_message, year)[0]
+    # chat_history.append((user_message, response +"\n"+ answer))
+    return rag_response
 
 chatbot_state = gr.State([])
 
@@ -186,26 +204,26 @@ with gr.Blocks(title="Reddit Election Analysis") as demo:
     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"
-            )
+        # 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..."
-            )
+        #     query_input = gr.Textbox(
+        #         label="Your Question",
+        #         placeholder="Ask about election comments or posts..."
+        #     )
 
-            submit_btn = gr.Button("Submit")
+        #     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?
-            """)
+        #     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",
@@ -213,7 +231,15 @@ with gr.Blocks(title="Reddit Election Analysis") as demo:
         #     )
         
         with gr.Column():
-            chatbot = gr.Chatbot(label="Chat History")
+            chatbot = gr.ChatInterface(chat_function, 
+                                       type="messages", 
+                                       addtional_inputs = [
+                                            year_selector = gr.Radio(
+                                                choices=["2016 Election", "2024 Election", "Comparison two years"],
+                                                label="Select Election Year",
+                                                value="2016 Election"
+                                            )
+                                       ])
             
     gr.Markdown("## Top works of the relevant Q&A")
     # with gr.Row():
@@ -254,12 +280,12 @@ with gr.Blocks(title="Reddit Election Analysis") as demo:
         outputs = [time_series_fig, linePlot_status_text]
     )
 
-    # Update both outputs when submit is clicked
-    submit_btn.click(
-        fn=chat_function,
-        inputs=[query_input, year_selector],
-        outputs= [chatbot, query_input]
-    )
+    # # Update both outputs when submit is clicked
+    # submit_btn.click(
+    #     fn=chat_function,
+    #     inputs=[query_input, year_selector],
+    #     outputs= chatbot
+    # )
 
 
 if __name__ == "__main__":