Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+import sqlite3
+import plotly.express as px
+import tempfile
+
+# Optional: OpenAI + LangChain imports if using NL -> SQL
+try:
+    from langchain_openai import OpenAI
+    from langchain_experimental.sql import SQLDatabaseChain
+    from langchain.sql_database import SQLDatabase
+    OPENAI_AVAILABLE = True
+except:
+    OPENAI_AVAILABLE = False
+
+def process_file(file, question):
+    if file is None or question.strip() == "":
+        return "Upload a file and ask a question.", None, None
+
+    # Read uploaded file
+    try:
+        fname = file.name.lower()
+        if fname.endswith(".csv"):
+            df = pd.read_csv(file.name)
+        else:
+            df = pd.read_excel(file.name)
+    except Exception as e:
+        return f"Error reading file: {e}", None, None
+
+    # Save to temporary SQLite file
+    try:
+        temp_db_file = tempfile.NamedTemporaryFile(suffix=".db").name
+        conn = sqlite3.connect(temp_db_file)
+        df.to_sql("data", conn, if_exists="replace", index=False)
+        conn.close()
+    except Exception as e:
+        return f"Error creating database: {e}", None, None
+
+    # Run natural language -> SQL if OpenAI available
+    result_text = "Rule-based: Showing first 5 rows"
+    result_df = df.head(5)  # Default fallback
+
+    if OPENAI_AVAILABLE:
+        try:
+            db = SQLDatabase.from_uri(f"sqlite:///{temp_db_file}")
+            llm = OpenAI(temperature=0)
+            db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=False)
+            result_text = db_chain.run(question)
+            # Execute the SQL safely to show result table
+            conn = sqlite3.connect(temp_db_file)
+            result_df = pd.read_sql_query("SELECT * FROM data LIMIT 100", conn)
+            conn.close()
+        except Exception as e:
+            result_text = f"OpenAI NL->SQL failed, showing fallback: {e}"
+            result_df = df.head(5)
+
+    # Visualization
+    fig = None
+    try:
+        numeric_cols = result_df.select_dtypes(include=['number']).columns.tolist()
+        categorical_cols = result_df.select_dtypes(include=['object']).columns.tolist()
+
+        if numeric_cols and categorical_cols:
+            x_col = categorical_cols[0]
+            y_col = numeric_cols[0]
+            fig = px.bar(result_df, x=x_col, y=y_col, title=f"{y_col} vs {x_col}")
+        elif numeric_cols:
+            fig = px.line(result_df[numeric_cols])
+        elif categorical_cols:
+            fig = px.histogram(result_df, x=categorical_cols[0])
+    except Exception as e:
+        fig = None  # fail silently for visualization
+
+    return result_text, result_df, fig
+
+# Gradio Interface
+file_input = gr.File(label="Upload CSV or Excel")
+question_input = gr.Textbox(label="Ask a question in natural language")
+output_text = gr.Textbox(label="Generated Result")
+output_table = gr.Dataframe(label="Query Result")
+output_plot = gr.Plot(label="Visualization")
+
+gr.Interface(
+    fn=process_file,
+    inputs=[file_input, question_input],
+    outputs=[output_text, output_table, output_plot],
+    live=False,
+    title="NL → SQL Query Generator + Visualization",
+    description="Upload CSV/Excel, ask natural language questions, and get SQL results with automatic visualizations."
+).launch()