Upload attribution.py

attribution.py

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+import gradio as gr
+from datasets import load_dataset
+
+import os
+import spaces
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer, BitsAndBytesConfig
+import torch
+from threading import Thread
+from sentence_transformers import SentenceTransformer
+from datasets import load_dataset
+import time
+
+token = os.environ["HF_TOKEN"]
+ST = SentenceTransformer("mixedbread-ai/mxbai-embed-large-v1")
+
+dataset = load_dataset("AI-4-Health/embedded-dataset")
+
+data = dataset["train"]
+data = data.add_faiss_index("embeddings") # column name that has the embeddings of the dataset
+
+
+model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
+
+# use quantization to lower GPU usage
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16
+)
+
+tokenizer = AutoTokenizer.from_pretrained(model_id,token=token)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+    quantization_config=bnb_config,
+    token=token
+)
+terminators = [
+    tokenizer.eos_token_id,
+    tokenizer.convert_tokens_to_ids("<|eot_id|>")
+]
+
+SYS_PROMPT = """You are an assistant for answering questions.
+You are given the extracted parts of a long document and a question. Provide a conversational answer.
+If you don't know the answer, just say "I do not know." Don't make up an answer."""
+
+
+
+def search(query: str, k: int = 3 ):
+    """a function that embeds a new query and returns the most probable results"""
+    embedded_query = ST.encode(query) # embed new query
+    scores, retrieved_examples = data.get_nearest_examples( # retrieve results
+        "embeddings", embedded_query, # compare our new embedded query with the dataset embeddings
+        k=k # get only top k results
+    )
+    return scores, retrieved_examples
+
+def format_prompt(prompt,retrieved_documents,k):
+    """using the retrieved documents we will prompt the model to generate our responses"""
+    PROMPT = f"Question:{prompt}\nContext:"
+    for idx in range(k) :
+        PROMPT+= f"{retrieved_documents['text'][idx]}\n"
+    return PROMPT
+
+
+
+TITLE = "# RAG"
+
+DESCRIPTION = """
+HPP Chatbot
+"""
+@spaces.GPU(duration=150)
+def talk(prompt):
+    k = 1 # number of retrieved documents
+    scores, retrieved_documents = search(prompt, k)
+    filename = retrieved_documents['filename'][0]  # Assuming filename is in the returned dictionary
+    print("filename is ", filename)
+    formatted_prompt = format_prompt(prompt, retrieved_documents, k)
+    formatted_prompt = formatted_prompt[:2000]  # to avoid GPU OOM
+    messages = [{"role":"system", "content":SYS_PROMPT}, {"role":"user", "content":formatted_prompt}]
+    # Tell the model to generate
+    input_ids = tokenizer.apply_chat_template(
+        messages,
+        add_generation_prompt=True,
+        return_tensors="pt"
+    ).to(model.device)
+    outputs = model.generate(
+        input_ids,
+        max_new_tokens=1024,
+        eos_token_id=terminators,
+        do_sample=True,
+        temperature=0.6,
+        top_p=0.9,
+    )
+    streamer = TextIteratorStreamer(
+        tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True
+    )
+    generate_kwargs = dict(
+        input_ids=input_ids,
+        streamer=streamer,
+        max_new_tokens=1024,
+        do_sample=True,
+        top_p=0.95,
+        temperature=0.75,
+        eos_token_id=terminators,
+    )
+    t = Thread(target=model.generate, kwargs=generate_kwargs)
+    t.start()
+
+    outputs = []
+    for text in streamer:
+        outputs.append(text)
+        #print(outputs)
+    return "".join(outputs), filename, filename
+
+def update_document(filename):
+    # Reads the content of the specified file for display
+    with open('datasets/'+filename, "r", encoding='iso-8859-15') as file:
+        content = file.read()
+    return content
+
+TITLE = "# RAG"
+DESCRIPTION = """
+HPP Chatbot
+"""
+with gr.Blocks() as demo:
+    with gr.Row():
+        prompt_input = gr.Textbox(label="Enter your prompt")
+        submit_button = gr.Button("Submit")
+        chat_output = gr.Textbox(label="Chat Response", lines=5)
+        filename = gr.Textbox(label="File Name", lines=1)
+        file_display = gr.Textbox(label="File Content", lines=10)
+
+    submit_button.click(
+        fn=talk,
+        inputs=prompt_input,
+        outputs=[chat_output, filename, file_display]
+    )
+
+    file_display.change(
+        fn=update_document,
+        inputs=filename,
+        outputs=file_display
+    )
+
+demo.launch(debug=True, share=True)