abhinand2 commited on
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e6125fa
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1 Parent(s): bfe9335

Update app.py

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Files changed (1) hide show
  1. app.py +32 -30
app.py CHANGED
@@ -1,43 +1,45 @@
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  import gradio as gr
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- from huggingface_hub import InferenceClient
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- """
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- For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
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- """
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- client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
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  def respond(
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- message,
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- history: list[tuple[str, str]],
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  system_message,
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  max_tokens,
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  temperature,
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  top_p,
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  ):
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- messages = [{"role": "system", "content": system_message}]
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-
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- for val in history:
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- if val[0]:
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- messages.append({"role": "user", "content": val[0]})
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- if val[1]:
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- messages.append({"role": "assistant", "content": val[1]})
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-
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- messages.append({"role": "user", "content": message})
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-
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- response = ""
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-
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- for message in client.chat_completion(
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- messages,
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- max_tokens=max_tokens,
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- stream=True,
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- temperature=temperature,
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- top_p=top_p,
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- ):
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- token = message.choices[0].delta.content
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-
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- response += token
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- yield response
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  """
 
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  import gradio as gr
 
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+ from db import get_db
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+ from chain import get_chain
 
 
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+ vectordb = get_db(
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+ chunk_size=1000,
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+ chunk_overlap=200,
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+ model_path = 'intfloat/multilingual-e5-large-instruct',
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+ )
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+
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+ chain = get_chain(
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+ vectordb,
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+ repo_id="HuggingFaceH4/zephyr-7b-beta",
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+ task="text-generation",
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+ max_new_tokens=512,
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+ top_k=30,
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+ temperature=0.1,
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+ repetition_penalty=1.03,
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+ search_type="mmr",
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+ k=3,
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+ fetch_k=5,
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+ template="""Use the following sentences of context to answer the question at the end.
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+ If you don't know the answer, that is if the answer is not in the context, then just say that you don't know, don't try to make up an answer.
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+ Always say "Thanks for asking!" at the end of the answer.
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+
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+ {context}
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+
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+ Question: {question}
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+ Helpful Answer:"""
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+ )
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+
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  def respond(
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+ question,
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+ _, # Ignore the message history parameter since we are doing one-off invocations
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  system_message,
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  max_tokens,
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  temperature,
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  top_p,
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  ):
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+ return rag_chain.invoke({'question': question})
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  """