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import json
import sys
import os
import pickle
import faiss
import gradio as gr
import numpy as np
import torch
from PIL import Image
from sentence_transformers import SentenceTransformer
from transformers import AutoImageProcessor, AutoTokenizer, AutoModel, AutoModelForCausalLM, BitsAndBytesConfig
from tqdm import tqdm
from datasets import load_dataset
from hazm import Normalizer
DATASET_NAME = 'parsi-ai-nlpclass/tourist-attraction-data'
TEST_DATA_NAME = 'parsi-ai-nlpclass/tourist-attraction-test-data'
dataset = load_dataset(DATASET_NAME, streaming=True)
test_data_name = load_dataset(TEST_DATA_NAME, streaming=True)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
vision_processor = AutoImageProcessor.from_pretrained('facebook/dinov2-base')
vision_model = AutoModel.from_pretrained('facebook/dinov2-base').to(device)
language_model = SentenceTransformer("xmanii/maux-gte-persian", trust_remote_code=True).to(device)
quantization_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
)
model = AutoModelForCausalLM.from_pretrained(
"universitytehran/PersianMind-v1.0",
quantization_config=quantization_config,
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(
"universitytehran/PersianMind-v1.0",
)
normalizer = Normalizer()
language_model.eval()
vision_model.eval()
# Load FAISS indices
text_index = faiss.read_index("text.index")
image_index = faiss.read_index("image.index")
# Load the index-item mapping
with open("idx_item_mapping.pkl", "rb") as f:
idx_item_mapping = pickle.load(f)
print("FAISS indices and index-item mapping loaded.")
def search_by_text(query_text, k=5):
"""
Searches the database for the top k items most similar to the query text.
Args:
query_text: The text query.
k: The number of top similar items to return.
Returns:
A list of dictionaries, where each dictionary contains the item details
for the top k similar items.
"""
normalized_query = normalizer.normalize(query_text)
query_embedding = language_model.encode(normalized_query)
query_embedding_np = query_embedding[np.newaxis, :]
faiss.normalize_L2(query_embedding_np)
distances, indices = text_index.search(query_embedding_np, 100)
unique_texts = set()
results = []
for idx in indices[0]:
text = idx_item_mapping[idx]
if text not in unique_texts:
unique_texts.add(text)
results.append(text)
if len(results) == k:
break
return results
def search_by_image(query_image, k=5):
"""
Searches the database for the top k items most similar to the query text.
Args:
query_text: The text query.
k: The number of top similar items to return.
Returns:
A list of dictionaries, where each dictionary contains the item details
for the top k similar items.
"""
inputs = vision_processor(images=query_image, return_tensors="pt").to(device) # Move image inputs to device
with torch.no_grad():
outputs = vision_model(**inputs)
image_embedding_np = outputs[0].mean(dim=1)[0].cpu().numpy()
query_embedding_np = image_embedding_np[np.newaxis, :]
faiss.normalize_L2(query_embedding_np)
# Search the FAISS index
distances, indices = image_index.search(query_embedding_np, 100)
# Get the top k items using the indices and the mapping
unique_texts = set()
results = []
for idx in indices[0]:
text = idx_item_mapping[idx]
if text not in unique_texts:
unique_texts.add(text)
results.append(text)
if len(results) == k:
break
return results
def rag_pipeline(question, image=None):
"""
Runs the RAG pipeline with the given question and optional image.
Args:
question: The text question.
image: Optional image input.
Returns:
The generated answer from the language model.
"""
retrieved_items = []
if image is not None:
retrieved_items.extend(search_by_image(image))
retrieved_items.extend(search_by_text(question))
TEMPLATE = "{context}\nYou: {prompt}\nPersianMind: "
CONTEXT = '\n'.join(retrieved_items)
PROMPT = '\n'.join([
question,
'به این سوال به فارسی جواب بده.'
])
model_input = TEMPLATE.format(context=CONTEXT, prompt=PROMPT)
input_tokens = tokenizer(model_input, return_tensors="pt")
input_tokens = input_tokens.to(device)
generate_ids = model.generate(**input_tokens, max_new_tokens=200, do_sample=False, repetition_penalty=1.1)
model_output = tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
return model_output[len(model_input):]
iface = gr.Interface(
fn=rag_pipeline,
inputs=[
gr.Textbox(label="Your Question"),
gr.Image(type="pil", label="Optional Image")
],
outputs=gr.Textbox(label="Answer"),
title="Tourist Attraction RAG Pipeline",
description="Ask a question about tourist attractions and optionally provide an image."
)
iface.launch(debug=True)
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