Upload 2 files
Browse files- app.py +190 -0
- requirements.txt +4 -0
app.py
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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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# """
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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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# if __name__ == "__main__":
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# demo.launch()
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import gradio as gr
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import torch
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from transformers import RagRetriever, RagSequenceForGeneration, AutoTokenizer
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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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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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def strip_title(title):
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if title.startswith('"'):
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title = title[1:]
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if title.endswith('"'):
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title = title[:-1]
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return title
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def retrieved_info(rag_model, query):
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# Tokenize query
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retriever_input_ids = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
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[query],
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return_tensors="pt",
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padding=True,
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truncation=True,
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)["input_ids"].to(device)
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# Retrieve documents
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question_enc_outputs = rag_model.rag.question_encoder(retriever_input_ids)
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question_enc_pool_output = question_enc_outputs[0]
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result = rag_model.retriever(
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retriever_input_ids,
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question_enc_pool_output.cpu().detach().to(torch.float32).numpy(),
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prefix=rag_model.rag.generator.config.prefix,
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n_docs=rag_model.config.n_docs,
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return_tensors="pt",
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)
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# Display retrieved documents including URLs
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all_docs = rag_model.retriever.index.get_doc_dicts(result.doc_ids)
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retrieved_context = []
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for docs in all_docs:
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titles = [strip_title(title) for title in docs["title"]]
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texts = docs["text"]
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for title, text in zip(titles, texts):
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#print(f"Title: {title}")
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#print(f"Context: {text}")
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retrieved_context.append(f"{title}: {text}")
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answer = retrieved_context
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return answer
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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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# Load model
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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dataset_path = "./sample/my_knowledge_dataset"
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index_path = "./sample/my_knowledge_dataset_hnsw_index.faiss"
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tokenizer = AutoTokenizer.from_pretrained("facebook/rag-sequence-nq")
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retriever = RagRetriever.from_pretrained("facebook/rag-sequence-nq", index_name="custom",
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passages_path = dataset_path,
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index_path = index_path,
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n_docs = 5)
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rag_model = RagSequenceForGeneration.from_pretrained('facebook/rag-sequence-nq', retriever=retriever)
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rag_model.retriever.init_retrieval()
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rag_model.to(device)
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if message: # If there's a user query
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response = retrieved_info(rag_model, message) # Get the answer from your local FAISS and Q&A model
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return response[0]
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# In case no message, return an empty string
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return ""
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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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# Custom title and description
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title = "🧠 Welcome to Your AI Knowledge Assistant"
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description = """
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HI!!, I am a chatbot, I retrieves relevant information from a custom dataset using RAG. Ask any question, and let me assist you.
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My capabilities and knowledge is limited right now because of computational resources. Originally I can acess more than a million files
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from my knowledge-base but, right now, I am limited to less than 1000 files. LET'S BEGGINNNN......
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"""
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demo = gr.ChatInterface(
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respond,
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type = 'messages',
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additional_inputs=[
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gr.Textbox(value="You are a helpful and friendly assistant.", 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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title=title,
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description=description,
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textbox=gr.Textbox(placeholder=["'What is the future of AI?' or 'App Development'"]),
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examples=[["✨Future of AI"], ["📱App Development"]],
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example_icons=["🤖", "📱"],
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theme="compact",
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)
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if __name__ == "__main__":
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demo.launch(share = True )
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requirements.txt
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torch == 2.4.0
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git+https://github.com/huggingface/transformers.git
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faiss-cpu
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datasets
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