Create app.py

app.py

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+# app.py
+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import sqlparse
+
+# Set page config
+st.set_page_config(
+    page_title="AI SQL Query Generator",
+    page_icon="🤖",
+    layout="centered"
+)
+
+# Load model and tokenizer
+@st.cache_resource
+def load_model():
+    model_name = "tscholak/cxmefzzi"  # Pre-trained text-to-SQL model
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+    return tokenizer, model
+
+# Format SQL output
+def format_sql(sql):
+    return sqlparse.format(sql, reindent=True, keyword_case='upper')
+
+# Generate SQL from natural language
+def generate_sql(input_text, tokenizer, model):
+    prefix = "Translate English to SQL: "
+    inputs = tokenizer(prefix + input_text, return_tensors="pt", max_length=512, truncation=True)
+    outputs = model.generate(**inputs, max_length=256)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# Streamlit UI
+def main():
+    st.title("🤖 AI-Powered SQL Query Generator")
+    st.markdown("Convert natural language questions to SQL queries")
+
+    # Load model
+    tokenizer, model = load_model()
+
+    # User input
+    user_input = st.text_area(
+        "Enter your question in natural language:",
+        placeholder="e.g., Show all customers from California who made purchases after January 2023",
+        height=150
+    )
+
+    # Generate button
+    if st.button("Generate SQL"):
+        if user_input.strip() == "":
+            st.warning("Please enter a question")
+        else:
+            with st.spinner("Generating SQL query..."):
+                try:
+                    # Generate and format SQL
+                    raw_sql = generate_sql(user_input, tokenizer, model)
+                    formatted_sql = format_sql(raw_sql)
+                    
+                    # Display results
+                    st.subheader("Generated SQL Query:")
+                    st.code(formatted_sql, language="sql")
+                    
+                    st.success("Query generated successfully!")
+                    
+                    # Show raw output for debugging
+                    with st.expander("Debug Info"):
+                        st.write(f"Model: tscholak/cxmefzzi")
+                        st.write(f"Raw Output: `{raw_sql}`")
+                except Exception as e:
+                    st.error(f"Error generating SQL: {str(e)}")
+
+    # Footer
+    st.markdown("---")
+    st.markdown("### How to use:")
+    st.markdown("1. Enter a question about data you want to query")
+    st.markdown("2. Click 'Generate SQL'")
+    st.markdown("3. Copy the generated SQL and use it in your database")
+    
+    st.markdown("### Example queries:")
+    st.code("Show the total sales per product category in 2022", language="text")
+    st.code("List employees hired before 2020 with salary above $50,000", language="text")
+    st.code("Count orders by customer country and sort descending", language="text")
+
+if __name__ == "__main__":
+    main()