Update app.py

app.py

@@ -1,56 +1,14 @@
 import streamlit as st
 import pandas as pd
-import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import os
-
-os.system("!GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/meta-llama/Llama-2-7b")
-
-hf_token = os.getenv("HF_TOKEN")
-token = os.getenv("HF_TOKEN")
-
-# Load the tokenizer
-model_name = "meta-llama/Llama-2-7b"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-# Load the model
-model = AutoModelForCausalLM.from_pretrained(model_name)
-
-# Apply dynamic quantization for CPU
-model = torch.quantization.quantize_dynamic(
-    model, {torch.nn.Linear}, dtype=torch.qint8
-)
-
-# Move model to CPU
-device = torch.device("cpu")
-model = model.to(device)
-
-# Set the padding token to the end-of-sequence token
-if tokenizer.pad_token is None:
-    tokenizer.pad_token = tokenizer.eos_token
+from transformers import pipeline
 
 # Load the anomalies data
 df = pd.read_csv('anomalies.csv', sep=',', decimal='.')
 
 # Function to generate a response
 def response(question):
-    prompt = f"Considerando os dados: {df.to_string(index=False)}, onde a coluna 'ds' está em formato DateTime, a coluna 'real' é o valor da despesa e a coluna 'group' é o grupo da despesa. Pergunta: {question}"
-    
-    inputs = tokenizer(prompt, return_tensors='pt', padding='max_length', truncation=True, max_length=256).to(device)
-    
-    generated_ids = model.generate(
-        inputs['input_ids'],
-        attention_mask=inputs['attention_mask'],
-        max_length=inputs['input_ids'].shape[1] + 50,
-        temperature=0.7,
-        top_p=0.9,
-        no_repeat_ngram_size=2,
-        num_beams=3,
-    )
-    
-    generated_text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
-    final_response = generated_text.split("Resposta:")[-1].split(".")[0] + "."
-    
+    tqa = pipeline(task="table-question-answering", model="google/tapas-large-finetuned-wtq")
+    final_rresposta = tqa(table=table, query=question)['cells'][0]
     return final_response
 
 # Streamlit interface