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import streamlit as st
from transformers import AutoModelForCausalLM, AutoTokenizer
# Set Streamlit page configuration
st.set_page_config(page_title="SQLCoder: AI-Powered SQL Code Generator", layout="wide")
# Function to load the model and tokenizer
@st.cache_resource
def load_model():
model_name = "defog/sqlcoder"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
device_map="auto",
offload_folder="./offload_weights", # Ensure weights are stored properly
low_cpu_mem_usage=True,
)
return tokenizer, model
# Load the model and tokenizer
try:
tokenizer, model = load_model()
except Exception as e:
st.error(f"Error loading model: {e}")
st.stop()
# Function to generate code using the model
def generate_code(prompt, max_length=150):
try:
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(
**inputs,
max_length=max_length,
num_return_sequences=1,
temperature=0.7, # Adjust temperature for creativity
top_k=50, # Limit sampling to top k tokens
)
return tokenizer.decode(outputs[0], skip_special_tokens=True)
except Exception as e:
raise RuntimeError(f"Error during code generation: {e}")
# Streamlit app layout
st.title("SQLCoder: AI-Powered SQL Code Generator")
st.write("Generate SQL queries and code snippets using the SQLCoder model.")
# Input prompt
prompt = st.text_area("Enter your query or code prompt:", height=150)
max_length = st.slider("Maximum Output Length", min_value=50, max_value=300, value=150, step=10)
# Generate button
if st.button("Generate Code"):
if prompt.strip():
with st.spinner("Generating code..."):
try:
generated_code = generate_code(prompt, max_length=max_length)
st.text_area("Generated Code:", value=generated_code, height=200)
except Exception as e:
st.error(f"Error generating code: {e}")
else:
st.warning("Please enter a prompt before generating code.")
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