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Baize
huggingface-web
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77ff0b3fac
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# -*- coding:utf-8 -*-
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import os
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import logging
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import sys
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import gradio as gr
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import torch
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from app_modules.utils import *
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from app_modules.presets import *
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from app_modules.overwrites import *
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logging.basicConfig(
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level=logging.DEBUG,
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format="%(asctime)s [%(levelname)s] [%(filename)s:%(lineno)d] %(message)s",
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)
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base_model = "decapoda-research/llama-7b-hf"
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adapter_model = "project-baize/baize-lora-7B"
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#tokenizer,model,device = load_tokenizer_and_model(base_model,adapter_model)
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def predict(text,
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chatbot,
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history,
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top_p,
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temperature,
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max_length_tokens,
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max_context_length_tokens,):
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if text=="":
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return history,history,"Empty Context"
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inputs = generate_prompt_with_history(text,history,tokenizer,max_length=max_context_length_tokens)
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if inputs is False:
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return [[x[0],convert_to_markdown(x[1])] for x in history]+[[text,"Sorry, the input is too long."]],history,"Generate Fail"
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else:
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prompt,inputs=inputs
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begin_length = len(prompt)
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input_ids = inputs["input_ids"].to(device)
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with torch.no_grad():
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for x in greedy_search(input_ids,model,tokenizer,stop_words=["[|Human|]", "[|AI|]"],max_length=max_length_tokens,temperature=temperature,top_p=top_p):
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if is_stop_word_or_prefix(x,["[|Human|]", "[|AI|]"]) is False:
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if "[|Human|]" in x:
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x = x[:x.index("[|Human|]")].strip()
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if "[|AI|]" in x:
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x = x[:x.index("[|AI|]")].strip()
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x = x.strip(" ")
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a, b= [[y[0],convert_to_markdown(y[1])] for y in history]+[[text, convert_to_markdown(x)]],history + [[text,x]]
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yield a, b, "Generating……"
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if shared_state.interrupted:
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shared_state.recover()
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try:
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yield a, b, "Stop Success"
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return
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except:
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pass
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print(prompt)
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print(x)
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print("="*80)
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try:
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yield a,b,"Generate Success"
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except:
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pass
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def retry(
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text,
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chatbot,
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history,
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top_p,
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temperature,
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max_length_tokens,
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max_context_length_tokens,
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):
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logging.info("Retry……")
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if len(history) == 0:
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yield chatbot, history, f"Empty context"
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return
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chatbot.pop()
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inputs = history.pop()[0]
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for x in predict(inputs,chatbot,history,top_p,temperature,max_length_tokens,max_context_length_tokens):
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yield x
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gr.Chatbot.postprocess = postprocess
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with open("assets/custom.css", "r", encoding="utf-8") as f:
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customCSS = f.read()
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with gr.Blocks(css=customCSS, theme=small_and_beautiful_theme) as demo:
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history = gr.State([])
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user_question = gr.State("")
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with gr.Row():
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gr.HTML(title)
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status_display = gr.Markdown("Success", elem_id="status_display")
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gr.Markdown(description_top)
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with gr.Row(scale=1).style(equal_height=True):
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with gr.Column(scale=5):
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with gr.Row(scale=1):
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chatbot = gr.Chatbot(elem_id="chuanhu_chatbot").style(height="80%")
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with gr.Row(scale=1):
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with gr.Column(scale=12):
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user_input = gr.Textbox(
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show_label=False, placeholder="Enter text"
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).style(container=False)
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with gr.Column(min_width=70, scale=1):
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submitBtn = gr.Button("Send")
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with gr.Column(min_width=70, scale=1):
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cancelBtn = gr.Button("Stop")
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with gr.Row(scale=1):
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emptyBtn = gr.Button(
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"🧹 New Conversation",
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)
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retryBtn = gr.Button("🔄 Regenerate")
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delLastBtn = gr.Button("🗑️ Remove Last Turn")
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with gr.Column():
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with gr.Column(min_width=50, scale=1):
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with gr.Tab(label="Parameter Setting"):
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gr.Markdown("# Parameters")
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top_p = gr.Slider(
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minimum=-0,
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maximum=1.0,
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value=0.95,
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step=0.05,
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interactive=True,
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label="Top-p",
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)
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temperature = gr.Slider(
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minimum=-0,
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maximum=2.0,
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value=1,
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step=0.1,
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interactive=True,
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label="Temperature",
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)
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max_length_tokens = gr.Slider(
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minimum=0,
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maximum=512,
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value=512,
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step=8,
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interactive=True,
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label="Max Generation Tokens",
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)
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max_context_length_tokens = gr.Slider(
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minimum=0,
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maximum=4096,
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value=2048,
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step=128,
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interactive=True,
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label="Max History Tokens",
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)
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gr.Markdown(description)
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predict_args = dict(
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fn=predict,
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inputs=[
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user_question,
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chatbot,
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history,
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top_p,
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temperature,
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max_length_tokens,
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max_context_length_tokens,
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],
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outputs=[chatbot, history, status_display],
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show_progress=True,
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)
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retry_args = dict(
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fn=retry,
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inputs=[
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user_input,
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chatbot,
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history,
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top_p,
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temperature,
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max_length_tokens,
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max_context_length_tokens,
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],
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outputs=[chatbot, history, status_display],
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show_progress=True,
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)
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reset_args = dict(
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fn=reset_textbox, inputs=[], outputs=[user_input, status_display]
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)
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# Chatbot
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cancelBtn.click(cancel_outputing, [], [ status_display])
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transfer_input_args = dict(
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fn=transfer_input, inputs=[user_input], outputs=[user_question, user_input, submitBtn, cancelBtn], show_progress=True
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)
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user_input.submit(**transfer_input_args).then(**predict_args)
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submitBtn.click(**transfer_input_args).then(**predict_args)
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emptyBtn.click(
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reset_state,
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outputs=[chatbot, history, status_display],
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show_progress=True,
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)
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emptyBtn.click(**reset_args)
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retryBtn.click(**retry_args)
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delLastBtn.click(
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delete_last_conversation,
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[chatbot, history],
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[chatbot, history, status_display],
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show_progress=True,
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)
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demo.title = "Baize"
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if __name__ == "__main__":
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reload_javascript()
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demo.queue(concurrency_count=CONCURRENT_COUNT).launch(
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share=True, favicon_path="./assets/favicon.ico", inbrowser=True
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)
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@ -0,0 +1,14 @@
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gradio
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mdtex2html
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pypinyin
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tiktoken
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socksio
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tqdm
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colorama
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duckduckgo_search
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Pygments
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llama_index
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langchain
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markdown
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markdown2
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