Update main.py
Browse files
main.py
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@@ -2,7 +2,7 @@ from langchain.chains import RetrievalQA, ConversationalRetrievalChain
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from langchain.vectorstores import Chroma
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from langchain.text_splitter import CharacterTextSplitter
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from langchain.document_loaders import DirectoryLoader, TextLoader,PyPDFLoader
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from transformers import pipeline
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from langchain.llms import HuggingFacePipeline
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from langchain.embeddings import HuggingFaceInstructEmbeddings
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import gradio as gr
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@@ -10,22 +10,41 @@ from InstructorEmbedding import INSTRUCTOR
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import torch
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
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model =
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model=model,
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tokenizer=tokenizer,
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max_length=
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temperature=0.5,
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top_p=0.95,
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repetition_penalty=1.15
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)
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local_llm = HuggingFacePipeline(pipeline=
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loader = PyPDFLoader('bipolar.pdf')
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# loader = TextLoader('info.txt')
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document = loader.load()
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from langchain.vectorstores import Chroma
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from langchain.text_splitter import CharacterTextSplitter
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from langchain.document_loaders import DirectoryLoader, TextLoader,PyPDFLoader
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from transformers import pipeline, AutoModelForCausalLM
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from langchain.llms import HuggingFacePipeline
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from langchain.embeddings import HuggingFaceInstructEmbeddings
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import gradio as gr
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import torch
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
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from transformers import AutoModelForSequenceClassification, AutoTokenizer
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# model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased-finetuned-mrpc")
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# tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased-finetuned-mrpc")
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#
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# tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-base")
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#
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# model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-base")
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model_id = "gpt2-medium"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id)
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pipeline = pipeline(
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"text-generation",
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model=model,
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tokenizer=tokenizer,
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max_length=100
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)
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# local_llm = HuggingFacePipeline(pipeline=pipeline)
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# pipe = pipeline(
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# "text2text-generation",
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# model=model,
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# tokenizer=tokenizer,
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# max_length=512,
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# temperature=0.5,
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# top_p=0.95,
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# repetition_penalty=1.15
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# )
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local_llm = HuggingFacePipeline(pipeline=pipeline)
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# print(local_llm('What is the capital of Syria?'))
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loader = PyPDFLoader('bipolar.pdf')
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# loader = TextLoader('info.txt')
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document = loader.load()
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