Update app.py

app.py

@@ -140,12 +140,19 @@ def qa_summarise(selected_queries, qa_llm_model, text_field, data_df):
     texts = qa_input_df[text_field].to_list()
     
     summary = generate_answer(qa_llm_model, texts, selected_queries[0], selected_queries, mode="multi_summarize")
+
+    doc_df = pd.DataFrame()
+    doc_df["number"] = [i+1 for i in range(len(texts))]
+    doc_df["text"] = texts
     
-    return summary
+    return summary, doc_df
 
             
 with gr.Blocks() as demo:
-    event_models = ["jayebaku/distilbert-base-multilingual-cased-crexdata-relevance-classifier"]
+    event_models = ["jayebaku/distilbert-base-multilingual-cased-crexdata-relevance-classifier",
+                    "jayebaku/distilbert-base-multilingual-cased-weather-classifier-2",
+                    "jayebaku/twitter-xlm-roberta-base-crexdata-relevance-classifier",
+                    "jayebaku/twhin-bert-base-crexdata-relevance-classifier"]
 
     with gr.Tab("Event Type Classification"):
         gr.Markdown(
@@ -254,7 +261,7 @@ with gr.Blocks() as demo:
         qa_button = gr.Button("Start QA")
         hsummary = gr.Textbox(label="Summary")
         
-        # qa_df = gr.DataFrame()
+        qa_df = gr.DataFrame()
         # answers_df = gr.DataFrame()
 
         addqry_button.click(add_query, inputs=[query_inp, queries_state], outputs=[selected_queries, queries_state])
@@ -263,7 +270,7 @@ with gr.Blocks() as demo:
         #                 outputs=[qa_df, answers_df, hsummary])
         qa_button.click(qa_summarise, 
                         inputs=[selected_queries, qa_llm_model, text_field, data], 
-                        outputs=hsummary)
+                        outputs=[hsummary, qa_df])
 
 
 demo.launch()