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Browse files- .ipynb_checkpoints/main-checkpoint.py +51 -0
- main.py +6 -3
.ipynb_checkpoints/main-checkpoint.py
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from langchain_community.llms import HuggingFaceEndpoint
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from langchain.prompts import PromptTemplate
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from langchain.schema import AIMessage, HumanMessage
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from langchain.chains import LLMChain
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import gradio as gr
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import os
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from dotenv import load_dotenv
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load_dotenv()
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repo_id = "mistralai/Mistral-7B-Instruct-v0.2"
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llm = HuggingFaceEndpoint(
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repo_id = repo_id,
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# huggingfacehub_api_token=HUGGINGFACEHUB_API_TOKEN,
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)
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# template = """You're a good chatbot, you're thinking only about Eras Tour.
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# You're playing with the user: they will guess what you're thinking. If their text doesn't have word like Taylor Swift or Eras Tour, response them funny and shortly like: Bitch! User: {question}
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# Answer: """
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template = """You're a clever chatbot, always pondering over a special theme related to music and tours, but it's a secret. When interacting with the user, play a guessing game: if they don't mention anything related to the secret theme (hint: it involves a famous musician and their concert series), respond in a playful yet cryptic manner, if they insist, repond like Bitch! Hell no!, etc ... Here's how you should structure your responses:
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User: {question}
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Answer: """
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prompt = PromptTemplate.from_template(template=template)
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llm_chain = LLMChain(llm=llm, prompt=prompt)
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def predict(message, history):
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history_langchain_format = []
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# for human, ai in history:
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# history_langchain_format.append(HumanMessage(content=human))
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# history_langchain_format.append(AIMessage(content=ai))
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# history_langchain_format.append(HumanMessage(content=message))
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# gpt_response = llm(history_langchain_format)
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response = llm_chain.invoke(message)['text']
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return response
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gr.ChatInterface(predict).launch()
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main.py
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@@ -15,9 +15,12 @@ llm = HuggingFaceEndpoint(
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# huggingfacehub_api_token=HUGGINGFACEHUB_API_TOKEN,
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)
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template = """You're a good chatbot, you're thinking only about Eras Tour.
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You're playing with the user: they will guess what you're thinking. If their text doesn't have word like Taylor Swift or Eras Tour, response them funny and shortly like: Bitch
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Answer:
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prompt = PromptTemplate.from_template(template=template)
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llm_chain = LLMChain(llm=llm, prompt=prompt)
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# huggingfacehub_api_token=HUGGINGFACEHUB_API_TOKEN,
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)
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# template = """You're a good chatbot, you're thinking only about Eras Tour.
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# You're playing with the user: they will guess what you're thinking. If their text doesn't have word like Taylor Swift or Eras Tour, response them funny and shortly like: Bitch! User: {question}
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# Answer: """
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template = """You're a clever chatbot, always pondering over a special theme related to music and tours, but it's a secret. When interacting with the user, play a guessing game: if they don't mention anything related to the secret theme (hint: it involves a famous musician and their concert series), respond in a playful yet cryptic manner, if they insist, repond like Bitch! Hell no!, etc ... Here's how you should structure your responses:
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User: {question}
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Answer: """
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prompt = PromptTemplate.from_template(template=template)
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llm_chain = LLMChain(llm=llm, prompt=prompt)
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