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Duplicate from mandar100/chatbot_bloom3B
Browse filesCo-authored-by: mandar <mandar100@users.noreply.huggingface.co>
- .gitattributes +34 -0
- README.md +13 -0
- app.py +96 -0
- requirements.txt +3 -0
.gitattributes
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README.md
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---
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title: Chatbot Bloom3B
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emoji: 💻
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colorFrom: gray
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colorTo: pink
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sdk: gradio
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sdk_version: 3.14.0
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app_file: app.py
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pinned: false
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duplicated_from: mandar100/chatbot_bloom3B
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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app.py
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#!/usr/bin/env python
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# coding: utf-8
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# In[ ]:
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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import gradio as gr
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import re
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def cleaning_history_tuple(history):
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s=sum(history,())
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s=list(s)
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s2=""
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for i in s:
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i=re.sub("\n", '', i)
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i=re.sub("<p>", '', i)
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i=re.sub("</p>", '', i)
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s2=s2+i+'\n'
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return s2
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def ai_output(string1,string2):
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a1=len(string1)
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a2=len(string2)
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string3=string2[a1:]
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sub1="A:"
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sub2="User"
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#sub3="\n"
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try:
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try:
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idx1=string3.index(sub1)
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response=string3[:idx1]
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return response
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except:
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idx1=string3.index(sub2)
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response=string3[:idx1]
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return response
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except:
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return string3
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model4 = AutoModelForCausalLM.from_pretrained("bigscience/bloom-3b")
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tokenizer4 = AutoTokenizer.from_pretrained("bigscience/bloom-3b")
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def predict(input,initial_prompt, temperature=0.7,top_p=1,top_k=5,max_tokens=64,no_repeat_ngram_size=1,num_beams=6,do_sample=True, history=[]):
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s = cleaning_history_tuple(history)
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s = s+ "\n"+ "User: "+ input + "\n" + "Assistant: "
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s2=initial_prompt+" " + s
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input_ids = tokenizer4.encode(str(s2), return_tensors="pt")
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response = model4.generate(input_ids, min_length = 10,
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max_new_tokens=int(max_tokens),
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top_k=int(top_k),
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top_p=float(top_p),
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temperature=float(temperature),
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no_repeat_ngram_size=int(no_repeat_ngram_size),
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num_beams = int(num_beams),
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do_sample = bool(do_sample),
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)
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response2 = tokenizer4.decode(response[0])
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print("Response after decoding tokenizer: ",response2)
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print("\n\n")
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response3=ai_output(s2,response2)
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input="User: "+input
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response3="Assistant: "+ response3
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history.append((input, response3))
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return history, history
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#gr.Interface(fn=predict,title="BLOOM-3b",
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# inputs=["text","text","text","text","text","text","text","text","text",'state'],
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#
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# outputs=["chatbot",'state']).launch()
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gr.Interface(inputs=[gr.Textbox(label="input", lines=1, value=""),
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gr.Textbox(label="initial_prompt", lines=1, value=prompt),
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gr.Textbox(label="temperature", lines=1, value=0.7),
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gr.Textbox(label="top_p", lines=1, value=1),
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gr.Textbox(label="top_k", lines=1, value=5),
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gr.Textbox(label="max_tokens", lines=1, value=64),
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gr.Textbox(label="no_repeat_ngram_size", lines=1, value=1),
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gr.Textbox(label="num_beams", lines=1, value=6),
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gr.Textbox(label="do_sample", lines=1, value="True"), 'state'],
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fn=predict, title="OPT-6.7B", outputs=["chatbot",'state']
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#inputs=["text","text","text","text","text","text","text","text","text",'state'],
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).launch()
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requirements.txt
ADDED
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torch
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gradio
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transformers
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