Update app.py

app.py

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+from dotenv import load_dotenv
+import os
+from sentence_transformers import SentenceTransformer
+import gradio as gr
+from sklearn.metrics.pairwise import cosine_similarity
+from groq import Groq
+import sqlite3
+import pandas as pd
+
+load_dotenv()
+api = os.getenv("groq_api_key")
+
+# 🔹 STEP 1: Create a sample in-memory SQLite database with mock data
+def setup_database():
+    conn = sqlite3.connect("college.db")
+    cursor = conn.cursor()
+
+    # Drop existing tables
+    cursor.execute("DROP TABLE IF EXISTS student;")
+    cursor.execute("DROP TABLE IF EXISTS employee;")
+    cursor.execute("DROP TABLE IF EXISTS course_info;")
+
+    # Student table
+    cursor.execute("""
+    CREATE TABLE student (
+        student_id INTEGER,
+        first_name TEXT,
+        last_name TEXT,
+        date_of_birth TEXT,
+        email TEXT,
+        phone_number TEXT,
+        major TEXT,
+        year_of_enrollment INTEGER
+    );
+    """)
+
+    cursor.execute("INSERT INTO student VALUES (1, 'Alice', 'Smith', '2000-05-01', 'alice@example.com', '1234567890', 'Computer Science', 2019);")
+
+    # Employee table
+    cursor.execute("""
+    CREATE TABLE employee (
+        employee_id INTEGER,
+        first_name TEXT,
+        last_name TEXT,
+        email TEXT,
+        department TEXT,
+        position TEXT,
+        salary REAL,
+        date_of_joining TEXT
+    );
+    """)
+
+    cursor.execute("INSERT INTO employee VALUES (101, 'John', 'Doe', 'john@college.edu', 'CSE', 'Professor', 80000, '2015-08-20');")
+
+    # Course table
+    cursor.execute("""
+    CREATE TABLE course_info (
+        course_id INTEGER,
+        course_name TEXT,
+        course_code TEXT,
+        instructor_id INTEGER,
+        department TEXT,
+        credits INTEGER,
+        semester TEXT
+    );
+    """)
+
+    cursor.execute("INSERT INTO course_info VALUES (501, 'AI Basics', 'CS501', 101, 'CSE', 4, 'Fall');")
+
+    conn.commit()
+    conn.close()
+
+# Call it once to setup
+setup_database()
+
+# 🔹 STEP 2: Embedding & LLM logic (unchanged mostly)
+def create_metadata_embeddings():
+    student = """Table: student..."""  # (same as your original metadata)
+    employee = """Table: employee..."""
+    course = """Table: course_info..."""
+    metadata_list = [student, employee, course]
+    model = SentenceTransformer('all-MiniLM-L6-v2')
+    embeddings = model.encode(metadata_list)
+    return embeddings, model, student, employee, course
+
+def find_best_fit(embeddings, model, user_query, student, employee, course):
+    query_embedding = model.encode([user_query])
+    similarities = cosine_similarity(query_embedding, embeddings)
+    best_match_table = similarities.argmax()
+    return [student, employee, course][best_match_table]
+
+def create_prompt(user_query, table_metadata):
+    system_prompt = """You are a SQL query generator specialized in generating SQL queries for a single table at a time. Your task is to accurately convert natural language queries into SQL statements based on the user's intent and the provided table metadata.
+  
+Rules:
+- Multi-Table Queries Allowed: You can generate queries involving multiple tables using appropriate SQL JOIN operations, based on the provided metadata.
+- Join Logic: Use INNER JOIN, LEFT JOIN, or other appropriate joins based on logical relationships (e.g., foreign keys like `student_id`, `instructor_id`, etc.) inferred from the metadata.
+- Metadata-Based Validation: Always ensure the generated query matches the table names, columns, and data types as described in the metadata.
+- User Intent: Accurately capture the user's requirements such as filters, sorting, aggregations, and selections across one or more tables.
+- SQL Syntax: Use standard SQL syntax that is compatible with most relational database systems.
+- Output Format: Provide only the SQL query in a single line. Do not include explanations or any extra text.
+  
+  Input Format:
+  User Query: The user's natural language request.
+  Table Metadata: The structure of the relevant table, including the table name, column names, and data types.
+  
+  Output Format:
+  SQL Query: A valid SQL query formatted for readability.
+  Do not output anything else except the SQL query.Not even a single word extra.Ouput the whole query in a single line only.
+  You are ready to generate SQL queries based on the user input and table metadata."""
+    user_prompt = f"User Query: {user_query}\nTable Metadata: {table_metadata}"
+    return system_prompt, user_prompt
+
+def generate_sql(system_prompt, user_prompt):
+    client = Groq(api_key=api)
+    chat_completion = client.chat.completions.create(
+        messages=[
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": user_prompt},
+        ],
+        model="llama3-70b-8192",
+    )
+    res = chat_completion.choices[0].message.content.strip()
+    if res.lower().startswith("select"):
+        return res
+    else:
+        return None
+
+# 🔹 STEP 3: Execute SQL and return results
+def execute_sql(sql_query):
+    try:
+        conn = sqlite3.connect("college.db")
+        df = pd.read_sql_query(sql_query, conn)
+        conn.close()
+        return df
+    except Exception as e:
+        return str(e)
+
+# 🔹 STEP 4: Final combined response
+def response(user_query):
+    embeddings, model, student, employee, course = create_metadata_embeddings()
+    table_metadata = find_best_fit(embeddings, model, user_query, student, employee, course)
+    system_prompt, user_prompt = create_prompt(user_query, table_metadata)
+    sql_query = generate_sql(system_prompt, user_prompt)
+
+    if sql_query:
+        result = execute_sql(sql_query)
+        return f"🧠 SQL Query:\n{sql_query}", result
+    else:
+        return "❌ Couldn't generate a valid SQL query.", None
+
+# 🔹 Gradio UI
+desc = """Ask a natural language question about students, employees, or courses. I'll generate and run a SQL query for you."""
+
+demo = gr.Interface(
+    fn=response,
+    inputs=gr.Textbox(label="Your Question"),
+    outputs=[
+        gr.Textbox(label="Generated SQL Query"),
+        gr.Dataframe(label="Query Result")
+    ],
+    title="Natural Language to SQL + Result",
+    description=desc
+)
+
+demo.launch(share=True)