Create app.py

app.py

@@ -0,0 +1,114 @@
+import gradio as gr
+import pandas as pd
+from functools import partial
+
+def save_chatbot_dialogue(chat_tutor, save_type):
+
+    formatted_convo = pd.DataFrame(chat_tutor.conversation_memory, columns=['user', 'chatbot'])
+
+    output_fname = f'tutoring_conversation.{save_type}'
+
+    if save_type == 'csv':
+        formatted_convo.to_csv(output_fname, index=False)
+    elif save_type == 'json':
+        formatted_convo.to_json(output_fname, orient='records')
+    elif save_type == 'txt':
+        temp = formatted_convo.apply(lambda x: 'User: {0}\nAI: {1}'.format(x[0], x[1]), axis=1)
+        temp = '\n\n'.join(temp.tolist())
+        with open(output_fname, 'w') as f:
+            f.write(temp)
+    else:
+        gr.update(value=None, visible=False)
+    
+    return gr.update(value=output_fname, visible=True)
+
+save_json = partial(save_chatbot_dialogue, save_type='json')
+save_txt = partial(save_chatbot_dialogue, save_type='txt')
+save_csv = partial(save_chatbot_dialogue, save_type='csv')
+
+
+
+class BasicTutor:
+    # create basic initialization function
+    def __init__(self):
+        self.conversation_memory = []
+        self.flattened_conversation = ''
+
+    def add_user_message(self, user_message):
+        self.conversation_memory.append([user_message, None])
+        self.flattened_conversation = self.flattened_conversation + '\n\n' + 'User: ' + user_message
+
+    def get_tutor_reply(self):
+        # get tutor message
+        tutor_message = "Yes"
+        # add tutor message to conversation memory
+        self.conversation_memory[-1][1] = tutor_message
+        self.flattened_conversation = self.flattened_conversation + '\nAI: ' + tutor_message
+
+    def forget_conversation(self):
+        self.conversation_memory = []
+        self.flattened_conversation = ''
+
+### Chatbot Functions ###
+
+def add_user_message(user_message, chat_tutor):
+  """Display user message and update chat history to include it."""
+  chat_tutor.add_user_message(user_message)
+  return chat_tutor.conversation_memory, chat_tutor
+
+def get_tutor_reply(chat_tutor):
+  chat_tutor.get_tutor_reply()
+  return chat_tutor.conversation_memory, chat_tutor
+
+# history is a list of list  [[user_input_str, bot_response_str], ...]
+def user(message, history):
+    return "", history + [[message, None]]
+
+def bot(history):
+    user_message = history[-1][0]
+    tutor_message = "You typed: " + user_message
+
+
+with gr.Blocks() as demo:
+    #initialize tutor (with state)
+    study_tutor = gr.State(BasicTutor())
+        
+    # Chatbot interface
+    gr.Markdown("""
+    ## Chat with the Model
+    Description here
+    """)
+
+    with gr.Row(equal_height=True):
+        with gr.Column(scale=2):
+          chatbot = gr.Chatbot()
+          with gr.Row():
+            user_chat_input = gr.Textbox(label="User input", scale=9)
+            user_chat_submit = gr.Button("Ask/answer model", scale=1)
+    
+    
+    async_response = user_chat_submit.click(add_user_message,
+                [user_chat_input, study_tutor],
+                [user_chat_input, chatbot, study_tutor], queue=False) \
+      .then(get_tutor_reply, [study_tutor], [user_chat_input, chatbot, study_tutor], queue=True)
+
+    
+    with gr.Blocks():
+        gr.Markdown("""
+        ## Export Your Chat History
+        Export your chat history as a .json, .txt, or .csv file
+        """)
+        with gr.Row():
+          export_dialogue_button_json = gr.Button("JSON")
+          export_dialogue_button_txt = gr.Button("TXT")
+          export_dialogue_button_csv = gr.Button("CSV")
+        
+        file_download = gr.Files(label="Download here",
+                                file_types=['.txt', '.csv', '.json'], type="file", visible=False)
+        
+        export_dialogue_button_json.click(save_json, study_tutor, file_download, show_progress=True)
+        export_dialogue_button_txt.click(save_txt, study_tutor, file_download, show_progress=True)
+        export_dialogue_button_csv.click(save_csv, study_tutor, file_download, show_progress=True)
+    
+demo.queue()
+demo.launch()