Bryan Bimantaka (Monash University)
commited on
Commit
·
fe7c49a
1
Parent(s):
fe2b85b
Add gradio
Browse files- .ipynb_checkpoints/app-checkpoint.py +3 -54
- .ipynb_checkpoints/requirements-checkpoint.txt +10 -1
- app.py +1 -0
- requirements.txt +1 -0
.ipynb_checkpoints/app-checkpoint.py
CHANGED
@@ -1,53 +1,14 @@
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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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# 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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# messages.append({"role": "user", "content": message})
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# response = ""
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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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# response += token
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# yield response
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from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, pipeline
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import transformers
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from sentence_transformers import SentenceTransformer
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from datasets import Dataset, Features, Value, Sequence
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import pandas as pd
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import faiss
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import os
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from langchain.document_loaders import TextLoader
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import torch
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ST_MODEL = "LazarusNLP/all-indo-e5-small-v4"
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BASE_MODEL = "meta-llama/Meta-Llama-3.1-8B-Instruct"
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@@ -146,18 +107,6 @@ For information on how to customize the ChatInterface, peruse the gradio docs: h
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demo = gr.ChatInterface(
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respond,
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textbox=gr.Textbox(placeholder="Enter message here", container=False, scale = 7),
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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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from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, pipeline
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from langchain_community.document_loaders import TextLoader # Diubah
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from huggingface_hub import InferenceClient # Pastikan ini ada
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import transformers
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from sentence_transformers import SentenceTransformer
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from datasets import Dataset, Features, Value, Sequence
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import pandas as pd
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import faiss
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import os
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import torch
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import gradio as gr
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ST_MODEL = "LazarusNLP/all-indo-e5-small-v4"
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BASE_MODEL = "meta-llama/Meta-Llama-3.1-8B-Instruct"
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demo = gr.ChatInterface(
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respond,
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textbox=gr.Textbox(placeholder="Enter message here", container=False, scale = 7),
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)
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.ipynb_checkpoints/requirements-checkpoint.txt
CHANGED
@@ -1 +1,10 @@
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huggingface_hub==0.22.2
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huggingface_hub==0.22.2
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transformers
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torch
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sentence-transformers
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gradio
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datasets
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langchain
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langchain-community
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faiss-cpu
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gradio
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app.py
CHANGED
@@ -8,6 +8,7 @@ import pandas as pd
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import faiss
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import os
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import torch
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ST_MODEL = "LazarusNLP/all-indo-e5-small-v4"
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BASE_MODEL = "meta-llama/Meta-Llama-3.1-8B-Instruct"
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import faiss
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import os
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import torch
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import gradio as gr
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ST_MODEL = "LazarusNLP/all-indo-e5-small-v4"
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BASE_MODEL = "meta-llama/Meta-Llama-3.1-8B-Instruct"
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requirements.txt
CHANGED
@@ -7,3 +7,4 @@ datasets
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langchain
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langchain-community
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faiss-cpu
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langchain
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langchain-community
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faiss-cpu
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gradio
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