Update app.py
Browse files
app.py
CHANGED
@@ -9,13 +9,11 @@ from langchain.chains import ConversationalRetrievalChain
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from ctransformers import AutoModelForCausalLM
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from langchain_g4f import G4FLLM
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from g4f import Provider, models
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import
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import requests
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# Define the path for generated embeddings
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DB_FAISS_PATH = 'vectorstore/db_faiss'
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return 'CJK UNIFIED' in unicodedata.name(character, '')
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# Load the model of choice
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def load_llm():
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# url = "https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML/blob/main/llama-2-7b-chat.ggmlv3.q4_K_M.bin" # 2.87G
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@@ -54,7 +52,7 @@ st.markdown(hide_streamlit_style, unsafe_allow_html=True)
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# Set the title for the Streamlit app
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st.title("Zendo美女チャットボックス")
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csv_url = "https://huggingface.co/spaces/uyen13/
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# csv_url="https://docs.google.com/uc?export=download&id=1fQ2v2n9zQcoi6JoOU3lCBDHRt3a1PmaE"
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# Define the path where you want to save the downloaded file
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@@ -92,14 +90,6 @@ chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=db.as_retriever
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def conversational_chat(query):
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query = "提供されたデータに基づいて,"+query
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result = chain({"question": query, "chat_history": st.session_state['history']})
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# i = 0
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# while i < len(result["answer"]):
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# character = input_string[i]
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# if is_japanese_character(character):
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# break
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# else:
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# result = chain({"question": query, "chat_history": st.session_state['history']})
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# i += 1
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st.session_state['history'].append((query, result["answer"]))
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return result["answer"]
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from ctransformers import AutoModelForCausalLM
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from langchain_g4f import G4FLLM
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from g4f import Provider, models
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# import spacy
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import requests
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# Define the path for generated embeddings
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DB_FAISS_PATH = 'vectorstore/db_faiss'
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# Load the model of choice
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def load_llm():
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# url = "https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML/blob/main/llama-2-7b-chat.ggmlv3.q4_K_M.bin" # 2.87G
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# Set the title for the Streamlit app
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st.title("Zendo美女チャットボックス")
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csv_url = "https://huggingface.co/spaces/uyen13/chatzendo/raw/main/testchatdata.csv"
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# csv_url="https://docs.google.com/uc?export=download&id=1fQ2v2n9zQcoi6JoOU3lCBDHRt3a1PmaE"
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# Define the path where you want to save the downloaded file
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def conversational_chat(query):
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query = "提供されたデータに基づいて,"+query
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result = chain({"question": query, "chat_history": st.session_state['history']})
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st.session_state['history'].append((query, result["answer"]))
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return result["answer"]
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