shoom013 commited on
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ebc1fba
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1 Parent(s): c604dce

Update app.py

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  1. app.py +32 -59
app.py CHANGED
@@ -1,62 +1,35 @@
1
  import gradio as gr
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- from huggingface_hub import InferenceClient
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-
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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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-
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-
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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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- 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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- """
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- For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
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- """
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- demo = gr.ChatInterface(
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- respond,
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- additional_inputs=[
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- gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
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- gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
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- gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
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- gr.Slider(
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- minimum=0.1,
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- maximum=1.0,
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- value=0.95,
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- step=0.05,
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- label="Top-p (nucleus sampling)",
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- ),
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- ],
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  )
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-
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- if __name__ == "__main__":
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- demo.launch()
 
 
 
 
 
 
 
 
 
 
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  import gradio as gr
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+ from transformers import pipeline
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+ from transformers.utils import logging
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+ from llama_index.embeddings.huggingface import HuggingFaceEmbedding
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+ import torch
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+ from llama_index.core import VectorStoreIndex
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+ from llama_index.core import Document
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+ from llama_index.core import Settings
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+ from llama_index.llms.huggingface import (
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+ HuggingFaceInferenceAPI,
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+ HuggingFaceLLM,
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+ )
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+ Settings.llm = HuggingFaceLLM(model_name="facebook/blenderbot-400M-distill",
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+ device_map="cpu",
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+ context_window=128,
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+ tokenizer_name="facebook/blenderbot-400M-distill"
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+ )
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+ Settings.embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")
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+ documents = [Document(text="Indian parliament elections happened in April-May 2024. BJP Party won.")]
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+ index = VectorStoreIndex.from_documents(
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+ documents,
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  )
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+ query_engine = index.as_query_engine()
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+ def rag(input_text, file):
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+ return query_engine.query(
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+ input_text
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+ )
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+
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+ iface = gr.Interface(fn=rag, inputs=[gr.Textbox(label="Question", lines=6), gr.File()],
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+ outputs=[gr.Textbox(label="Result", lines=6)],
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+ title="Answer my question",
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+ description= "CoolChatBot"
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+ )
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+ iface.launch()