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app.py

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+import gradio as gr
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+import torch
+import pandas as pd
+
+# Load model and tokenizer
+model_name = "tscholak/cxmefzzi"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+device = torch.device("cpu")
+
+def generate_sql(natural_language, csv_file):
+    # Read uploaded dataset
+    if csv_file is not None:
+        df = pd.read_csv(csv_file.name)
+        table_columns = ", ".join(df.columns)
+        context = f"The table has columns: {table_columns}."
+    else:
+        context = ""
+
+    prompt = f"{context} Convert the question to SQL: {natural_language}"
+
+    inputs = tokenizer(prompt, return_tensors="pt").to(device)
+    outputs = model.generate(**inputs, max_length=128)
+    sql_query = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+    return sql_query
+
+# Gradio UI
+iface = gr.Interface(
+    fn=generate_sql,
+    inputs=[
+        gr.Textbox(label="Enter your question (natural language)"),
+        gr.File(label="Upload your CSV file (optional)")
+    ],
+    outputs=gr.Textbox(label="Generated SQL Query"),
+    title="🧠 Natural Language to SQL Converter",
+    description="Upload a dataset and type your question in natural language to generate an SQL query automatically.",
+)
+
+if __name__ == "__main__":
+    iface.launch()

requirements.txt

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+transformers
+torch
+gradio
+pandas