Merge pull request #3 from huggingface/feat/upgrade-gradio-flow
Browse files- .gitignore +1 -0
- README.md +1 -1
- app/app.py +152 -23
.gitignore
CHANGED
@@ -160,3 +160,4 @@ cython_debug/
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# and can be added to the global gitignore or merged into this file. For a more nuclear
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# option (not recommended) you can uncomment the following to ignore the entire idea folder.
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#.idea/
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# and can be added to the global gitignore or merged into this file. For a more nuclear
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# option (not recommended) you can uncomment the following to ignore the entire idea folder.
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#.idea/
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+
user_feedback
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README.md
CHANGED
@@ -4,7 +4,7 @@ emoji: 🚀
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colorFrom: blue
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colorTo: gray
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sdk: gradio
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-
sdk_version: 5.
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app_file: app/app.py
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pinned: false
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---
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colorFrom: blue
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colorTo: gray
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sdk: gradio
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+
sdk_version: 5.10.0
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app_file: app/app.py
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pinned: false
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---
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app/app.py
CHANGED
@@ -1,16 +1,18 @@
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import os
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import uuid
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from base64 import b64encode
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from datetime import datetime
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from mimetypes import guess_type
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from pathlib import Path
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import gradio as gr
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from huggingface_hub import InferenceClient
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from pandas import DataFrame
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from feedback import save_feedback
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client = InferenceClient(
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token=os.getenv("HF_TOKEN"),
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model=(
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@@ -30,7 +32,7 @@ def add_user_message(history, message):
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return history, gr.MultimodalTextbox(value=None, interactive=False)
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-
def
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messages = []
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current_role = None
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current_message_content = []
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@@ -84,46 +86,152 @@ def _process_content(content) -> str | list[str]:
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return content
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def
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response = client.chat.completions.create(
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messages=messages,
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max_tokens=2000,
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stream=False,
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)
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content = response.choices[0].message.content
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# TODO: Add a response to the user message
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-
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message = gr.ChatMessage(role="assistant", content=content)
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history.append(message)
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return history
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def wrangle_like_data(x: gr.LikeData, history) -> DataFrame:
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"""Wrangle conversations and liked data into a DataFrame"""
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-
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output_data = []
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for idx, message in enumerate(history):
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if idx == liked_index:
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message["metadata"] = {"title": "liked" if x.liked else "disliked"}
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rating = message["metadata"].get("title")
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if rating == "liked":
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message["rating"] = 1
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elif rating == "disliked":
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message["rating"] = -1
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else:
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message["rating"] =
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output_data.append(
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dict(
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)
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return history, DataFrame(data=output_data)
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def submit_conversation(dataframe, session_id):
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""" "Submit the conversation to dataset repo"""
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if dataframe.empty:
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@@ -142,7 +250,14 @@ def submit_conversation(dataframe, session_id):
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return (gr.Dataframe(value=None, interactive=False), [])
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-
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##############################
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# Chatbot
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##############################
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chatbot = gr.Chatbot(
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elem_id="chatbot",
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bubble_full_width=False,
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type="messages",
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)
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chat_input = gr.MultimodalTextbox(
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submit_btn=True,
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)
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-
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fn=add_user_message, inputs=[chatbot, chat_input], outputs=[chatbot, chat_input]
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)
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-
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-
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)
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bot_msg.then(lambda: gr.Textbox(interactive=True), None, [chat_input])
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-
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##############################
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# Deal with feedback
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##############################
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-
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chatbot.like(
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fn=wrangle_like_data,
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like_user_message=False,
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)
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-
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-
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-
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fn=submit_conversation,
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inputs=[dataframe, session_id],
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outputs=[dataframe, chatbot],
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import os
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+
import random
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import uuid
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from base64 import b64encode
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from datetime import datetime
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from mimetypes import guess_type
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from pathlib import Path
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from typing import Optional
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import gradio as gr
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from feedback import save_feedback
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from gradio.components.chatbot import Option
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from huggingface_hub import InferenceClient
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from pandas import DataFrame
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client = InferenceClient(
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token=os.getenv("HF_TOKEN"),
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model=(
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return history, gr.MultimodalTextbox(value=None, interactive=False)
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def format_history_as_messages(history: list):
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messages = []
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current_role = None
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current_message_content = []
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return content
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def add_fake_like_data(history: list, session_id: str, liked: bool = False) -> None:
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data = {
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"index": len(history) - 1,
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"value": history[-1],
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"liked": True,
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}
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_, dataframe = wrangle_like_data(
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gr.LikeData(target=None, data=data), history.copy()
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)
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submit_conversation(dataframe, session_id)
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def respond_system_message(
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history: list, temperature: Optional[float] = None, seed: Optional[int] = None
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) -> list: # -> list:
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"""Respond to the user message with a system message
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Return the history with the new message"""
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messages = format_history_as_messages(history)
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response = client.chat.completions.create(
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messages=messages,
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max_tokens=2000,
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stream=False,
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seed=seed,
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temperature=temperature,
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)
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content = response.choices[0].message.content
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message = gr.ChatMessage(role="assistant", content=content)
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history.append(message)
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return history
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def update_dataframe(dataframe: DataFrame, history: list) -> DataFrame:
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"""Update the dataframe with the new message"""
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data = {
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"index": 9999,
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"value": None,
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"liked": False,
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}
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_, dataframe = wrangle_like_data(
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gr.LikeData(target=None, data=data), history.copy()
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)
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return dataframe
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def wrangle_like_data(x: gr.LikeData, history) -> DataFrame:
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"""Wrangle conversations and liked data into a DataFrame"""
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if isinstance(x.index, int):
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liked_index = x.index
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else:
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liked_index = x.index[0]
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output_data = []
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for idx, message in enumerate(history):
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if isinstance(message, gr.ChatMessage):
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message = message.__dict__
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if idx == liked_index:
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message["metadata"] = {"title": "liked" if x.liked else "disliked"}
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if not isinstance(message["metadata"], dict):
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message["metadata"] = message["metadata"].__dict__
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rating = message["metadata"].get("title")
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if rating == "liked":
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message["rating"] = 1
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elif rating == "disliked":
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message["rating"] = -1
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else:
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message["rating"] = 0
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message["chosen"] = ""
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message["rejected"] = ""
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if message["options"]:
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for option in message["options"]:
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if not isinstance(option, dict):
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option = option.__dict__
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message[option["label"]] = option["value"]
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else:
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if message["rating"] == 1:
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message["chosen"] = message["content"]
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elif message["rating"] == -1:
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message["rejected"] = message["content"]
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output_data.append(
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dict(
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[(k, v) for k, v in message.items() if k not in ["metadata", "options"]]
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)
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)
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return history, DataFrame(data=output_data)
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def wrangle_edit_data(
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x: gr.EditData, history: list, dataframe: DataFrame, session_id: str
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) -> list:
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"""Edit the conversation and add negative feedback if assistant message is edited, otherwise regenerate the message
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Return the history with the new message"""
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if isinstance(x.index, int):
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index = x.index
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else:
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index = x.index[0]
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original_message = gr.ChatMessage(
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role="assistant", content=dataframe.iloc[index]["content"]
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).__dict__
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+
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if history[index]["role"] == "user":
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# Add feedback on original and corrected message
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add_fake_like_data(history[: index + 2], session_id, liked=True)
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add_fake_like_data(history[: index + 1] + [original_message], session_id)
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history = respond_system_message(
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history[: index + 1],
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temperature=random.randint(1, 100) / 100,
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seed=random.randint(0, 1000000),
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)
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return history
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else:
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# Add feedback on original and corrected message
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add_fake_like_data(history[: index + 1], session_id, liked=True)
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add_fake_like_data(history[:index] + [original_message], session_id)
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history = history[: index + 1]
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# add chosen and rejected options
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history[-1]["options"] = [
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Option(label="chosen", value=x.value),
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Option(label="rejected", value=original_message["content"]),
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]
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return history
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def wrangle_retry_data(
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x: gr.RetryData, history: list, dataframe: DataFrame, session_id: str
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) -> list:
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"""Respond to the user message with a system message and add negative feedback on the original message
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Return the history with the new message"""
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add_fake_like_data(history, session_id)
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# Return the history without a new message
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history = respond_system_message(
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history[:-1],
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temperature=random.randint(1, 100) / 100,
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seed=random.randint(0, 1000000),
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)
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return history, update_dataframe(dataframe, history)
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+
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+
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def submit_conversation(dataframe, session_id):
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""" "Submit the conversation to dataset repo"""
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if dataframe.empty:
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return (gr.Dataframe(value=None, interactive=False), [])
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css = """
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.options {
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display: none !important;
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}
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"""
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with gr.Blocks(css=css) as demo:
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##############################
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# Chatbot
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##############################
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chatbot = gr.Chatbot(
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elem_id="chatbot",
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editable="all",
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bubble_full_width=False,
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type="messages",
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feedback_options=["Like", "Dislike"],
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)
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chat_input = gr.MultimodalTextbox(
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submit_btn=True,
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)
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dataframe = gr.Dataframe(wrap=True)
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submit_btn = gr.Button(
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value="Submit conversation",
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)
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##############################
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# Deal with feedback
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##############################
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chat_input.submit(
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fn=add_user_message,
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inputs=[chatbot, chat_input],
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outputs=[chatbot, chat_input],
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).then(respond_system_message, chatbot, chatbot, api_name="bot_response").then(
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lambda: gr.Textbox(interactive=True), None, [chat_input]
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).then(update_dataframe, inputs=[dataframe, chatbot], outputs=[dataframe])
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chatbot.like(
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fn=wrangle_like_data,
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like_user_message=False,
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)
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chatbot.retry(
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fn=wrangle_retry_data,
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inputs=[chatbot, dataframe, session_id],
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outputs=[chatbot, dataframe],
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)
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chatbot.edit(
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fn=wrangle_edit_data,
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inputs=[chatbot, dataframe, session_id],
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outputs=[chatbot],
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).then(update_dataframe, inputs=[dataframe, chatbot], outputs=[dataframe])
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+
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submit_btn.click(
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fn=submit_conversation,
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inputs=[dataframe, session_id],
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outputs=[dataframe, chatbot],
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