Delete functions_huggingface.py

functions_huggingface.py

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