Update app.py

app.py

@@ -1,9 +1,10 @@
 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 (persistent in Space) ---
+# --- Setup SQLite database ---
 engine = create_engine("sqlite:///data.db")
 metadata_obj = MetaData()
 
@@ -12,7 +13,7 @@ students = Table(
     "students",
     metadata_obj,
     Column("student_id", Integer, primary_key=True),
-    Column("student_name", String(32), primary_key=True),
+    Column("student_name", String(32), nullable=False),
 )
 metadata_obj.create_all(engine)
 
@@ -34,49 +35,53 @@ subjects = Table(
     metadata_obj,
     Column("student_id", Integer, primary_key=True),
     Column("subject_name", String(32), primary_key=True),
-    Column("marks", Float),
+    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},
-    {"student_id": 1, "subject_name": "English", "marks": 88.5},
-    {"student_id": 2, "subject_name": "Math", "marks": 72.0},
-    {"student_id": 2, "subject_name": "Science", "marks": 81.0},
-    {"student_id": 3, "subject_name": "Math", "marks": 85.0},
-    {"student_id": 3, "subject_name": "History", "marks": 90.0},
-    {"student_id": 4, "subject_name": "English", "marks": 78.0},
-    {"student_id": 4, "subject_name": "Science", "marks": 82.0},
+    {"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: SQL query string.
-
-    Returns:
-        List of tuples with query results.
-    """
+    """Executes SQL queries on students and subjects and returns list of tuples."""
     try:
-        print(f"🧩 Executing query: {query}")  # Debug log
+        print(f"🧩 Executing query: {query}")
         with engine.connect() as con:
             rows = con.execute(text(query))
-            results = [tuple(row) for row in rows]  # Keep results as tuples
+            results = [tuple(row) for row in rows]
         return results or []
     except Exception as e:
         return [f"⚠️ SQL Error: {str(e)}"]
 
-# Dynamically describe tables
-updated_description = "Allows SQL queries on the following tables:\n"
+# 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)]
@@ -92,16 +97,24 @@ agent = CodeAgent(
     tools=[sql_engine],
     model=InferenceClientModel(
         "meta-llama/Meta-Llama-3-8B-Instruct",
-        api_key=os.environ.get("HF_TOKEN")  # Use secret from Space
+        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)
-        # Convert tuples to readable string if result is a list of tuples
         if isinstance(result, list):
             result_str = "\n".join(str(r) for r in result)
             return result_str or "No results."
@@ -109,12 +122,18 @@ def ask_agent(question: str) -> str:
     except Exception as e:
         return f"⚠️ Error: {str(e)}"
 
-demo = gr.Interface(
-    fn=ask_agent,
-    inputs=gr.Textbox(label="Ask a question (e.g., Who scored highest in Math?)"),
-    outputs=gr.Textbox(label="Agent response"),
-    title="🧠 Text-to-SQL Agent",
-    description="Ask natural-language questions about students, subjects, and marks. Powered by smolagents + Meta-Llama-3.",
-)
+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}")
+    question_input.submit(fn=ask_agent, inputs=question_input, outputs=answer_output)
 
 demo.launch()