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abhishek23HF
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210585c
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Parent(s):
300bf99
Create app.py
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app.py
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import streamlit as st
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import torch
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from peft import PeftModel, PeftConfig
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "NL2SQL_BLOOMZ-3B"
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HUGGING_FACE_USER_NAME = "abhishek23HF"
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peft_model_id = f"{HUGGING_FACE_USER_NAME}/{model_name}"
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config = PeftConfig.from_pretrained(peft_model_id)
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model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_8bit=False)
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tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
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# Load the Lora model
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model = PeftModel.from_pretrained(model, peft_model_id)
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from IPython.display import display, Markdown
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def make_inference(db_id, question):
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batch = tokenizer(f"""
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### INSTRUCTION\n
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Below is a User Question for a SQL DATABASE. Your job is to write a SQL Query for the given question from the user for that particular Database.
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\n\n
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### DATABASE_ID:\n{db_id}\n
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### USER QUESTION:\n{question}\n\n
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### SQL QUERY:\n
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""", return_tensors='pt')
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with torch.cuda.amp.autocast():
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output_tokens = model.generate(**batch, max_new_tokens=200)
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return tokenizer.decode(output_tokens[0], skip_special_tokens=True)
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# Create two text input boxes
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text_input_db_id= st.text_input("DB ID")
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text_input_question = st.text_input("User Query")
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# make_inference(your_db_id_here, your_db_query_here)
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# Display the text input boxes
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if st.button('Submit'):
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st.write(make_inference(text_input_db_id, text_input_question))
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