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Delete functions_huggingface.py
Browse files- functions_huggingface.py +0 -109
functions_huggingface.py
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from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
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import sqlite3
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from langchain_core.prompts import ChatPromptTemplate
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from langchain_core.output_parsers import StrOutputParser
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from langchain_core.runnables import RunnablePassthrough
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import re
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import gradio as gr
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# Load the Llama model and tokenizer
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model_name = "meta-llama/Llama-3.3-70B-Instruct"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")
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generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
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# Initialize database connection
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db_path = "Spring_2025_courses.db"
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connection = sqlite3.connect(db_path)
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def get_schema():
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"""Retrieve database schema"""
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cursor = connection.cursor()
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cursor.execute("SELECT name FROM sqlite_master WHERE type='table';")
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tables = cursor.fetchall()
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schema = {}
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for table_name in tables:
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table_name = table_name[0]
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cursor.execute(f"PRAGMA table_info({table_name});")
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columns = cursor.fetchall()
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schema[table_name] = [column[1] for column in columns]
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return schema
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def run_query(query):
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"""Execute SQL query"""
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cursor = connection.cursor()
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cursor.execute(query)
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return cursor.fetchall()
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# Prompt templates
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system_prompt = """
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You are a SQLite expert. Given an input question, create one syntactically correct SQLite query to run. Generate only one query. No preamble.
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Here is the relevant table information:
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schema: {schema}
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Tips:
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- Use LIKE instead of = in the queries
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Write only one SQLite query that would answer the user's question.
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"""
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human_prompt = """Based on the table schema below, write a SQL query that would answer the user's question:
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{schema}
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Question: {question}
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SQL Query:"""
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prompt = ChatPromptTemplate.from_messages([
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("system", system_prompt),
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("human", human_prompt),
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])
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# Build query generation chain
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sql_generator = (
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RunnablePassthrough.assign(schema=get_schema)
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| prompt
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| StrOutputParser()
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)
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def generate_sql(question):
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"""Generate SQL query from question"""
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schema = get_schema()
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input_prompt = system_prompt.format(schema=schema, question=question)
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response = generator(input_prompt, max_length=512, num_return_sequences=1)
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return response[0]['generated_text']
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def execute_safe_query(question):
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"""Safely execute a natural language query"""
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try:
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# Generate SQL query
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sql_query = generate_sql(question)
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# Validate SQL query
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if not sql_query.strip().lower().startswith("select"):
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return {"error": "Only SELECT queries are allowed.", "query": sql_query, "result": None}
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# Execute query
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result = run_query(sql_query)
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return {"error": None, "query": sql_query, "result": result}
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except Exception as e:
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return {"error": str(e), "query": None, "result": None}
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# Deploy using Gradio
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def query_interface(question):
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response = execute_safe_query(question)
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if response['error']:
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return f"Error: {response['error']}\nGenerated Query: {response['query']}"
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return f"Query: {response['query']}\nResult: {response['result']}"
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iface = gr.Interface(
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fn=query_interface,
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inputs="text",
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outputs="text",
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title="SQLite Query Generator with Llama 3.3",
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description="Ask a natural language question about the Spring 2025 courses database and get the SQL query and results.",
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)
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if __name__ == "__main__":
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iface.launch()
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