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import gradio as gr
from langchain_community.vectorstores import FAISS
from langchain_community.embeddings import HuggingFaceEmbeddings
from transformers import AutoModelForCausalLM, AutoTokenizer
import zipfile
import os
import torch
# Percorsi ZIP per manuali e problemi
zip_path_m = "faiss_manual_index.zip"
faiss_manual_index = "faiss_manual_index"
zip_path_p = "faiss_problems_index.zip"
faiss_problems_index = "faiss_problems_index"
# Estrazione dei file ZIP se necessario
for zip_path, output_dir in [(zip_path_m, faiss_manual_index), (zip_path_p, faiss_problems_index)]:
if not os.path.exists(output_dir):
os.makedirs(output_dir)
if os.path.exists(zip_path):
with zipfile.ZipFile(zip_path, 'r') as zip_ref:
zip_ref.extractall(output_dir)
# Caricamento del modello di embedding
embedding_model = HuggingFaceEmbeddings(model_name="sentence-transformers/LaBSE")
# Caricamento dei vectorstore FAISS
manual_vectorstore = FAISS.load_local(faiss_manual_index, embedding_model, allow_dangerous_deserialization=True)
problems_vectorstore = FAISS.load_local(faiss_problems_index, embedding_model, allow_dangerous_deserialization=True)
# Caricamento del modello GPT-J da Hugging Face
model_name = "EleutherAI/gpt-j-6B"
# Forza l'uso della CPU
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.float32, # float32 per la CPU
device_map={"": "cpu"} # Specifica CPU come dispositivo
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto")
# Funzione per la ricerca e il riassunto
def search_and_summarize(query):
# Ricerca nei manuali e problemi
manual_results = manual_vectorstore.similarity_search(query, k=2)
manual_output = "\n\n".join([doc.page_content for doc in manual_results])
problems_results = problems_vectorstore.similarity_search(query, k=2)
problems_output = "\n\n".join([doc.page_content for doc in problems_results])
combined_text = f"Manual Results:\n{manual_output}\n\nProblems Results:\n{problems_output}"
# Generazione del riassunto con GPT-J
input_text = f"Riassumi le seguenti informazioni:\n{combined_text}\n\nRiassunto:"
inputs = tokenizer(input_text, return_tensors="pt").to("cuda")
output = model.generate(inputs.input_ids, max_length=300, temperature=0.7)
summary = tokenizer.decode(output[0], skip_special_tokens=True)
return manual_output, problems_output, summary
# Interfaccia Gradio
iface = gr.Interface(
fn=search_and_summarize,
inputs=gr.Textbox(lines=2, placeholder="Enter your question here..."),
outputs=[
gr.Textbox(label="Manual Results"),
gr.Textbox(label="Issues Results"),
gr.Textbox(label="Summary by GPT-J")
],
examples=[
["How to change the knife?"],
["What are the safety precautions for using the machine?"],
["How can I get help with the machine?"]
],
title="Manual Querying System with GPT-J Summarization",
description="Enter a question to get information from the manual and the common issues, summarized by GPT-J."
)
# Avvia l'app Gradio
iface.launch()
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