Deploy SQLCoder with Streamlit

streamlit_app.py

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+import streamlit as st
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Set page configuration
+st.set_page_config(page_title="SQLCoder", layout="wide")
+
+# Load the model and tokenizer
+@st.cache_resource
+def load_model():
+    model_name = "defog/sqlcoder-7b-2"
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")
+    return tokenizer, model
+
+tokenizer, model = load_model()
+
+# Define the function to generate code
+def generate_code(prompt, max_length=150):
+    inputs = tokenizer(prompt, return_tensors="pt")
+    outputs = model.generate(**inputs, max_length=max_length, num_return_sequences=1)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# Streamlit app layout
+st.title("SQLCoder: AI-Powered SQL Code Generator")
+st.write("Generate SQL queries and code snippets using the SQLCoder model.")
+
+# Input for user 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.")