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import gradio as gr
import pandas as pd
import numpy as np
from transformers import pipeline, BertTokenizer, BertModel
import faiss
import torch
import json
import spaces
# Load CSV data
data = pd.read_csv('RBDx10kstats.csv')
# Function to safely convert JSON strings to numpy arrays
def safe_json_loads(x):
try:
return np.array(json.loads(x), dtype=np.float32) # Ensure the array is of type float32
except json.JSONDecodeError as e:
print(f"Error decoding JSON: {e}")
return np.array([], dtype=np.float32) # Return an empty array or handle it as appropriate
# Apply the safe_json_loads function to the embedding column
data['embedding'] = data['embedding'].apply(safe_json_loads)
# Filter out any rows with empty embeddings
data = data[data['embedding'].apply(lambda x: x.size > 0)]
# Initialize FAISS index
dimension = len(data['embedding'].iloc[0])
res = faiss.StandardGpuResources() # use a single GPU
# Create FAISS index
if faiss.get_num_gpus() > 0:
gpu_index = faiss.IndexFlatL2(dimension)
gpu_index = faiss.index_cpu_to_gpu(res, 0, gpu_index) # move to GPU
else:
gpu_index = faiss.IndexFlatL2(dimension) # fall back to CPU
# Ensure embeddings are stacked as float32
embeddings = np.vstack(data['embedding'].values).astype(np.float32)
gpu_index.add(embeddings)
# Check if GPU is available
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Load QA model
qa_model = pipeline("question-answering", model="distilbert-base-uncased-distilled-squad", device=0 if torch.cuda.is_available() else -1)
# Load BERT model and tokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained('bert-base-uncased').to(device)
# Function to embed the question using BERT
def embed_question(question, model, tokenizer):
inputs = tokenizer(question, return_tensors='pt').to(device)
with torch.no_grad():
outputs = model(**inputs)
return outputs.last_hidden_state.mean(dim=1).cpu().numpy().astype(np.float32)
# Function to retrieve the relevant document and generate a response
@spaces.GPU(duration=120)
def retrieve_and_generate(question):
# Embed the question
question_embedding = embed_question(question, model, tokenizer)
# Search in FAISS index
_, indices = gpu_index.search(question_embedding, k=1)
# Retrieve the most relevant document
relevant_doc = data.iloc[indices[0][0]]
# Use the QA model to generate the answer
context = relevant_doc['Abstract']
response = qa_model(question=question, context=context)
return response['answer']
# Create a Gradio interface
interface = gr.Interface(
fn=retrieve_and_generate,
inputs=gr.Textbox(lines=2, placeholder="Ask a question about the documents..."),
outputs="text",
title="RAG Chatbot",
description="Ask questions about the documents in the CSV file."
)
# Launch the Gradio app
interface.launch() |