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import gradio as gr
from huggingface_hub import InferenceClient
import pandas as pd
import transformers
import torch
from sentence_transformers import SentenceTransformer, util
"""
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")
# Load the SBERT model
sbert_model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
# Function to initialize the Llama 3 8B model pipeline
def initiate_pipeline():
model = "meta-llama/Meta-Llama-3-8B-Instruct"
device = "cuda:0" if torch.cuda.is_available() else "cpu"
return transformers.pipeline(
"text-generation",
model=model,
model_kwargs={"torch_dtype": torch.bfloat16},
device=device,
)
# Initialize the model
llama_model = initiate_pipeline()
# Load the Q&A pairs from the CSV
qa_data = pd.read_csv("rag_juri_cv.csv")
# Function to retrieve the top 5 relevant Q&A pairs using Sentence-BERT
def retrieve_top_k(query, k=5):
# Combine the questions from the CSV into a list
questions = qa_data['QUESTION'].tolist()
# Encode the questions and the query using Sentence-BERT
question_embeddings = sbert_model.encode(questions, convert_to_tensor=True)
query_embedding = sbert_model.encode(query, convert_to_tensor=True)
# Compute cosine similarities between the query and all questions
cosine_scores = util.pytorch_cos_sim(query_embedding, question_embeddings).flatten()
# Get the indices of the top k most similar questions
top_k_indices = torch.topk(cosine_scores, k=k).indices.cpu()
# Retrieve the corresponding Q&A pairs
top_k_qa = qa_data.iloc[top_k_indices]
return top_k_qa
def chatbot(query):
# Retrieve the top 5 relevant Q&A pairs
top_k_qa = retrieve_top_k(query)
# Generate the prefix, body, and suffix for the prompt
prefix = """
<|begin_of_text|><|start_header_id|>user<|end_header_id|>You are a chatbot specialized in answering questions about Juri Grosjean's CV.
Please only use the information provided in the context to answer the question.
Here is the question to answer:
""" + query + "\n\n"
context = "This is the context information to answer the question:\n"
for index, row in top_k_qa.iterrows():
context += f"Information {index}: {row['ANSWER']}\n\n"
suffix = "<|eot_id|><|start_header_id|>assistant<|end_header_id|>"
prompt = prefix + context + suffix
# Generate a response
outputs = llama_model(
prompt,
max_new_tokens=500,
do_sample=True,
temperature=0.6,
top_p=0.9,
)
# Extract and return the chatbot's answer
output = outputs[0]["generated_text"]
return output.split("assistant")[-1].strip()
# Set up the Gradio interface
demo = gr.Interface(
fn=chatbot,
inputs=gr.Textbox(lines=5, placeholder="Ask a question about Juri Grosjean's CV"),
outputs="text"
)
if __name__ == "__main__":
demo.launch() |