Update app.py

app.py

@@ -1,147 +1,30 @@
-import os
-import gradio as gr
-import pandas as pd
-from sqlalchemy import create_engine, MetaData, Table, Column, String, Integer, Float, insert, text, inspect
-from smolagents import tool, CodeAgent, InferenceClientModel
-
-# --- Setup SQLite database ---
-engine = create_engine("sqlite:///data.db")
-metadata_obj = MetaData()
-
-# --- Create students table ---
-students = Table(
-    "students",
-    metadata_obj,
-    Column("student_id", Integer, primary_key=True),
-    Column("student_name", String(32), nullable=False),
-)
-metadata_obj.create_all(engine)
-
-# Insert sample student data
-student_rows = [
-    {"student_id": 1, "student_name": "Alice Johnson"},
-    {"student_id": 2, "student_name": "Bob Smith"},
-    {"student_id": 3, "student_name": "Charlie Brown"},
-    {"student_id": 4, "student_name": "Diana Prince"},
-]
-for row in student_rows:
-    stmt = insert(students).values(**row)
-    with engine.begin() as conn:
-        conn.execute(stmt)
-
-# --- Create subjects table ---
-subjects = Table(
-    "subjects",
-    metadata_obj,
-    Column("student_id", Integer, primary_key=True),
-    Column("subject_name", String(32), primary_key=True),
-    Column("marks", Float, nullable=False),
-    Column("teacher", String(32), nullable=False),
-)
-metadata_obj.create_all(engine)
-
-# Insert sample subject/marks data
-subject_rows = [
-    {"student_id": 1, "subject_name": "Math", "marks": 95.0, "teacher": "Mr. Adams"},
-    {"student_id": 1, "subject_name": "English", "marks": 88.5, "teacher": "Ms. Baker"},
-    {"student_id": 1, "subject_name": "Science", "marks": 92.0, "teacher": "Dr. Carter"},
-    {"student_id": 2, "subject_name": "Math", "marks": 72.0, "teacher": "Mr. Adams"},
-    {"student_id": 2, "subject_name": "Science", "marks": 81.0, "teacher": "Dr. Carter"},
-    {"student_id": 3, "subject_name": "Math", "marks": 85.0, "teacher": "Mr. Adams"},
-    {"student_id": 3, "subject_name": "History", "marks": 90.0, "teacher": "Mrs. Davis"},
-    {"student_id": 4, "subject_name": "English", "marks": 78.0, "teacher": "Ms. Baker"},
-    {"student_id": 4, "subject_name": "Science", "marks": 82.0, "teacher": "Dr. Carter"},
-]
-for row in subject_rows:
-    stmt = insert(subjects).values(**row)
-    with engine.begin() as conn:
-        conn.execute(stmt)
-
-# --- Convert tables to DataFrames for front-end display ---
-def fetch_table(table_name: str) -> pd.DataFrame:
-    with engine.connect() as con:
-        rows = con.execute(text(f"SELECT * FROM {table_name}"))
-        df = pd.DataFrame(rows.fetchall(), columns=rows.keys())
-    return df
-
-students_df = fetch_table("students")
-subjects_df = fetch_table("subjects")
-
-# --- Define SQL tool for the agent ---
-@tool
-def sql_engine(query: str) -> list:
-    """
-    Executes SQL queries on the available tables: students and subjects.
-
-    Args:
-        query (str): The SQL query string to execute.
-
-    Returns:
-        list: List of tuples containing the query results.
-    """
-    try:
-        print(f"🧩 Executing query: {query}")
-        with engine.connect() as con:
-            rows = con.execute(text(query))
-            results = [tuple(row) for row in rows]
-        return results or []
-    except Exception as e:
-        return [f"⚠️ SQL Error: {str(e)}"]
-
-# Dynamically describe tables for the agent
-updated_description = "You can run SQL queries on these tables:\n"
-inspector = inspect(engine)
-for table in ["students", "subjects"]:
-    columns_info = [(col["name"], col["type"]) for col in inspector.get_columns(table)]
-    table_description = f"\n\nTable '{table}':\nColumns:\n" + "\n".join(
-        [f" - {name}: {col_type}" for name, col_type in columns_info]
-    )
-    updated_description += table_description
-
-sql_engine.description = updated_description
-
-# --- Create the agent ---
-agent = CodeAgent(
-    tools=[sql_engine],
-    model=InferenceClientModel(
-        "meta-llama/Meta-Llama-3-8B-Instruct",
-        api_key=os.environ.get("HF_TOKEN")
-    ),
-)
-
-# --- Sample prompts to display ---
-sample_prompts = [
-    "Which student scored highest in Math?",
-    "List all subjects and marks for Alice Johnson.",
-    "Who is the teacher of Science for Diana Prince?",
-    "Show students who scored more than 80 in Science.",
-    "Average marks per subject."
-]
-
-# --- Define Gradio interface ---
-def ask_agent(question: str) -> str:
-    """Ask the AI agent a question and return its answer."""
-    try:
-        result = agent.run(question)
-        if isinstance(result, list):
-            result_str = "\n".join(str(r) for r in result)
-            return result_str or "No results."
-        return str(result)
-    except Exception as e:
-        return f"⚠️ Error: {str(e)}"
-
 with gr.Blocks() as demo:
     gr.Markdown("## 🏫 Student Marks SQL Agent")
-    gr.Markdown("### Students Table")
-    gr.DataFrame(value=students_df, interactive=False)
-    gr.Markdown("### Subjects Table")
-    gr.DataFrame(value=subjects_df, interactive=False)
-    gr.Markdown("### Ask questions about students, subjects, marks, and teachers")
-    question_input = gr.Textbox(label="Your question", placeholder="e.g., Which student scored highest in Math?")
-    answer_output = gr.Textbox(label="Agent response")
-    gr.Markdown("### Sample Prompts")
-    for prompt in sample_prompts:
-        gr.Markdown(f"- {prompt}")
+
+    with gr.Row():
+        # --- Left column: tables (smaller) ---
+        with gr.Column(scale=1):
+            gr.Markdown("### Students Table")
+            gr.DataFrame(value=students_df, interactive=False, height=200)
+            gr.Markdown("### Subjects Table")
+            gr.DataFrame(value=subjects_df, interactive=False, height=200)
+            gr.Markdown("### Sample Prompts")
+            for prompt in sample_prompts:
+                gr.Markdown(f"- {prompt}")
+
+        # --- Right column: question & response (larger) ---
+        with gr.Column(scale=2):
+            gr.Markdown("### Ask questions about students, subjects, marks, and teachers")
+            question_input = gr.Textbox(
+                label="Your question",
+                placeholder="e.g., Which student scored highest in Math?",
+                lines=2
+            )
+            answer_output = gr.Textbox(
+                label="Agent response",
+                lines=20
+            )
+
     question_input.submit(fn=ask_agent, inputs=question_input, outputs=answer_output)
 
 demo.launch()