Update app.py

app.py

@@ -1,56 +1,53 @@
-import gradio as gr
-import pandas as pd
-from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
-import sqlite3
-
-# Load model and tokenizer
-model_name = "mrm8488/t5-base-finetuned-wikiSQL"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
-
-def generate_sql_query(natural_language, data):
-    # Load uploaded CSV
-    df = pd.read_csv(data.name)
-    
-    # Create in-memory SQLite DB
-    conn = sqlite3.connect(":memory:")
-    df.to_sql("data_table", conn, index=False, if_exists="replace")
-    
-    # Create schema description
-    schema = ", ".join([f"{col}" for col in df.columns])
-    
-    # Combine user query and schema
-    input_text = f"translate English to SQL: {natural_language} | table columns: {schema}"
-    
-    # Generate SQL query
-    inputs = tokenizer(input_text, return_tensors="pt")
-    outputs = model.generate(**inputs, max_length=256)
-    sql_query = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    
-    try:
-        # Execute the generated SQL query
-        result_df = pd.read_sql_query(sql_query, conn)
-    except Exception as e:
-        result_df = pd.DataFrame({"Error": [str(e)]})
-    
-    conn.close()
-    return sql_query, result_df.head()
-
-# Gradio UI
-iface = gr.Interface(
-    fn=generate_sql_query,
-    inputs=[
-        gr.Textbox(label="Enter your question (Natural Language)", placeholder="e.g., Show customers with age > 30"),
-        gr.File(label="Upload CSV dataset")
-    ],
-    outputs=[
-        gr.Textbox(label="Generated SQL Query"),
-        gr.Dataframe(label="Query Result")
-    ],
-    title="🧠 Natural Language to SQL Generator",
-    description="Upload a CSV file and ask questions in plain English. The app converts them into SQL and shows the result.",
-    allow_flagging="never"
-)
-
-if __name__ == "__main__":
-    iface.launch()
+import gradio as gr
+import pandas as pd
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import sqlite3
+
+# Load model
+model_name = "mrm8488/t5-base-finetuned-wikiSQL"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+
+def nl_to_sql(question, file):
+    try:
+        df = pd.read_csv(file.name)
+    except Exception as e:
+        return f"Error reading CSV: {e}", pd.DataFrame()
+    
+    # Create SQLite DB
+    conn = sqlite3.connect(":memory:")
+    df.to_sql("data_table", conn, index=False, if_exists="replace")
+
+    # Schema description
+    schema = ", ".join(df.columns)
+    text = f"translate English to SQL: {question} | table columns: {schema}"
+
+    inputs = tokenizer(text, return_tensors="pt")
+    outputs = model.generate(**inputs, max_length=256)
+    sql_query = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+    # Try executing SQL query
+    try:
+        result = pd.read_sql_query(sql_query, conn)
+    except Exception as e:
+        result = pd.DataFrame({"Error": [str(e)]})
+
+    conn.close()
+    return sql_query, result.head()
+
+iface = gr.Interface(
+    fn=nl_to_sql,
+    inputs=[
+        gr.Textbox(label="Ask your question (Natural Language)", placeholder="e.g., Show customers older than 30"),
+        gr.File(label="Upload your CSV file")
+    ],
+    outputs=[
+        gr.Textbox(label="Generated SQL Query"),
+        gr.Dataframe(label="Result Preview")
+    ],
+    title="🧠 Natural Language to SQL Generator",
+    description="Upload a CSV and ask questions in plain English. Generates SQL and shows results instantly."
+)
+
+if __name__ == "__main__":
+    iface.launch()