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# import gradio as gr
# from huggingface_hub import InferenceClient
# """
# 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
# """
# client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
# def respond(
# message,
# history: list[tuple[str, str]],
# system_message,
# max_tokens,
# temperature,
# top_p,
# ):
# messages = [{"role": "system", "content": system_message}]
# for val in history:
# if val[0]:
# messages.append({"role": "user", "content": val[0]})
# if val[1]:
# messages.append({"role": "assistant", "content": val[1]})
# messages.append({"role": "user", "content": message})
# response = ""
# for message in client.chat_completion(
# messages,
# max_tokens=max_tokens,
# stream=True,
# temperature=temperature,
# top_p=top_p,
# ):
# token = message.choices[0].delta.content
# response += token
# yield response
# """
# For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
# """
# demo = gr.ChatInterface(
# respond,
# additional_inputs=[
# gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
# gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
# gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
# gr.Slider(
# minimum=0.1,
# maximum=1.0,
# value=0.95,
# step=0.05,
# label="Top-p (nucleus sampling)",
# ),
# ],
# )
# if __name__ == "__main__":
# demo.launch()
import gradio as gr
import torch
from transformers import RagRetriever, RagSequenceForGeneration, AutoTokenizer
"""
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
"""
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def strip_title(title):
if title.startswith('"'):
title = title[1:]
if title.endswith('"'):
title = title[:-1]
return title
def retrieved_info(rag_model, query):
# Tokenize query
retriever_input_ids = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
[query],
return_tensors="pt",
padding=True,
truncation=True,
)["input_ids"].to(device)
# Retrieve documents
question_enc_outputs = rag_model.rag.question_encoder(retriever_input_ids)
question_enc_pool_output = question_enc_outputs[0]
result = rag_model.retriever(
retriever_input_ids,
question_enc_pool_output.cpu().detach().to(torch.float32).numpy(),
prefix=rag_model.rag.generator.config.prefix,
n_docs=rag_model.config.n_docs,
return_tensors="pt",
)
# Display retrieved documents including URLs
all_docs = rag_model.retriever.index.get_doc_dicts(result.doc_ids)
retrieved_context = []
for docs in all_docs:
titles = [strip_title(title) for title in docs["title"]]
texts = docs["text"]
for title, text in zip(titles, texts):
#print(f"Title: {title}")
#print(f"Context: {text}")
retrieved_context.append(f"{title}: {text}")
answer = retrieved_context
return answer
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens ,
temperature,
top_p,
):
# Load model
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
dataset_path = "./sample/my_knowledge_dataset"
index_path = "./sample/my_knowledge_dataset_hnsw_index.faiss"
tokenizer = AutoTokenizer.from_pretrained("facebook/rag-sequence-nq")
retriever = RagRetriever.from_pretrained("facebook/rag-sequence-nq", index_name="custom",
passages_path = dataset_path,
index_path = index_path,
n_docs = 5)
rag_model = RagSequenceForGeneration.from_pretrained('facebook/rag-sequence-nq', retriever=retriever)
rag_model.retriever.init_retrieval()
rag_model.to(device)
if message: # If there's a user query
response = retrieved_info(rag_model, message) # Get the answer from your local FAISS and Q&A model
return response[0]
# In case no message, return an empty string
return ""
"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
# Custom title and description
title = "🧠 Welcome to Your AI Knowledge Assistant"
description = """
HI!!, I am a chatbot, I retrieves relevant information from a custom dataset using RAG. Ask any question, and let me assist you.
My capabilities and knowledge is limited right now because of computational resources. Originally I can acess more than a million files
from my knowledge-base but, right now, I am limited to less than 1000 files. LET'S BEGGINNNN......
"""
demo = gr.ChatInterface(
respond,
type = 'messages',
additional_inputs=[
gr.Textbox(value="You are a helpful and friendly assistant.", label="System message"),
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.95,
step=0.05,
label="Top-p (nucleus sampling)",
),
],
title=title,
description=description,
textbox=gr.Textbox(placeholder=["'What is the future of AI?' or 'App Development'"]),
examples=[["✨Future of AI"], ["📱App Development"]],
example_icons=["🤖", "📱"],
theme="compact",
)
if __name__ == "__main__":
demo.launch(share = True ) |