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import gradio as gr |
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import os |
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import textwrap |
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from langchain.embeddings import HuggingFaceEmbeddings |
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from langchain.llms import OpenAI |
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from langchain.prompts import PromptTemplate |
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from langchain.chains import RetrievalQA |
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from langchain.llms import OpenAI |
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from langchain.docstore.document import Document |
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from langchain.vectorstores import FAISS |
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def setup_generator(api_key): |
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os.environ["OPENAI_API_KEY"] = api_key |
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embeddings = HuggingFaceEmbeddings(model_name="dangvantuan/sentence-camembert-base") |
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db = FAISS.load_local("faiss_index", embeddings) |
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prompt_template = """Use the following pieces of context to answer the question at the end. |
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Give many informations as possible. |
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If you don't know the answer, just say that you don't know, don't try to make up an answer. |
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{context} |
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Question: {question} |
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Answer in French:""" |
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PROMPT = PromptTemplate( |
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template=prompt_template, input_variables=["context", "question"] |
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) |
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chain_type_kwargs = {"prompt": PROMPT} |
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qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type="stuff", |
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retriever=db.as_retriever(), |
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chain_type_kwargs=chain_type_kwargs, |
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return_source_documents=True) |
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return qa |
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def generate_text(api_key, query): |
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qa = setup_generator(api_key) |
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result = qa({"query": query}) |
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res = result["result"] |
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source = '\n'.join([meta.metadata["title"] for meta in result["source_documents"]]) |
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formatted_text = '\n'.join(textwrap.wrap(res, width=90)) |
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return formatted_text, source |
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iface = gr.Interface( |
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fn=generate_text, |
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inputs=[gr.inputs.Textbox(lines=1, placeholder="Input API Key..."), |
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gr.inputs.Textbox(lines=3, placeholder="Input Text...", |
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default="comment vois tu le future de l'intelligence artificielle?")], |
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outputs=[gr.outputs.Textbox(label="Generated Text"), |
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gr.outputs.Textbox(label="Source")] |
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) |
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iface.launch() |
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