Update app.py

app.py

@@ -1,277 +1,280 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
-import pandas as pd
-import io
-import mysql.connector
-
-# Hugging Face API Key (Replace with your actual key)
-client = InferenceClient(model="mistralai/Mistral-7B-Instruct-v0.3", token=HF_API_KEY)
-
-def classify_task(user_input):
-    """Classifies user task using Mistral."""
-    prompt = f"""
-You are an AI assistant that classifies user requests related to SQL and databases.  
-Your task is to categorize the input into one of the following options:  
-
-- **generate_sql** → If the user asks to generate **SQL syntax**, like SELECT, INSERT, CREATE TABLE, etc.  
-- **create_table** → If the user explicitly wants to **create** a database/table on a local MySQL server.  
-- **generate_demo_data_db** → If the user wants to insert demo data into a database.  
-- **generate_demo_data_csv** → If the user wants to generate demo data in CSV format.  
-- **analyze_data** → If the user asks for insights, trends, or statistical analysis of data.  
-
-**Examples:**
-1. "Give me SQL syntax to create a student table" → **generate_sql**  
-2. "Create a student table in my database" → **create_table**  
-3. "Insert some demo data in my database" → **generate_demo_data_db**  
-4. "Generate sample student data in CSV format" → **generate_demo_data_csv**  
-5. "Analyze student marks and trends" → **analyze_data**  
-
-**User Input:** {user_input}  
-
-**Output Format:** Return only the category name without any explanations.
-"""
-
-    response = client.text_generation(prompt, max_new_tokens=20).strip()
-    return response
-def generate_sql_query(user_input):
-    """Generates SQL queries using Mistral."""
-    prompt = f"Generate SQL syntax for: {user_input}"
-    return client.text_generation(prompt, max_new_tokens=200).strip()
-def generate_sql_query_for_create(user_input):
-    """Generates SQL queries using Mistral."""
-    prompt = f"""
-    Generate **only** the SQL syntax for: {user_input}
-    
-    **Rules:**
-    - No explanations, no bullet points, no extra text.
-    - Return **only valid SQL**.
-    
-    **Example Input:**  
-    "Create a student table with name, age, and email."
-    
-    **Example Output:**  
-    ```sql
-    CREATE TABLE student (
-        student_id INT PRIMARY KEY AUTO_INCREMENT,
-        name VARCHAR(100) NOT NULL,
-        age INT NOT NULL,
-        email VARCHAR(255) UNIQUE NOT NULL
-    );
-    ```
-    """
-
-    response = client.text_generation(prompt, max_new_tokens=200).strip()
-
-    # Remove unnecessary text (if any)
-    if "```sql" in response:
-        response = response.split("```sql")[1].split("```")[0].strip()
-
-    return response
-
-import pymysql
-
-def create_table(user_input, db_user, db_pass, db_host, db_name):
-    try:
-        # Validate inputs
-        if not all([db_user, db_pass, db_host, db_name, user_input]):
-            return "Please provide all required inputs (database credentials and table structure).", None
-
-        # Generate SQL schema using Mistral
-        
-        schema_response = generate_sql_query_for_create(user_input)
-        print(schema_response)
-        # Validate schema using sqlparse
-        parsed_schema = sqlparse.parse(schema_response)
-        if not parsed_schema:
-            return "Error: Could not generate a valid table schema.", None
-
-        # Connect to MySQL Server
-        connection = pymysql.connect(host=db_host, user=db_user, password=db_pass)
-        cursor = connection.cursor()
-
-        # Create Database if it doesn't exist
-        cursor.execute(f"CREATE DATABASE IF NOT EXISTS {db_name}")
-        connection.commit()
-        connection.close()
-
-        # Connect to the specified database
-        connection = pymysql.connect(host=db_host, user=db_user, password=db_pass, database=db_name)
-        cursor = connection.cursor()
-
-        # Execute the generated CREATE TABLE statement
-        cursor.execute(schema_response)
-        connection.commit()
-
-        return "Table created successfully.", None
-    
-    except pymysql.MySQLError as err:
-        return f"Error: {err}", None
-    
-    finally:
-        if 'connection' in locals() and connection.open:
-            cursor.close()
-            connection.close()
-
-import mysql.connector
-import re
-import sqlparse  # Install via: pip install sqlparse
-
-def generate_demo_data_db(user_input, db_user, db_pass, db_host, db_name, num_rows=10):
-    """Generates and inserts structured demo data into a database using LLM."""
-    
-    if not all([db_user, db_pass, db_name]):
-        return "Please provide database credentials.", None
-    
-    # Generate column definitions using LLM
-    schema_prompt = f"""
-    Extract column names and types from the following request:
-    
-    "{user_input}"
-    
-    **Output Format:**  
-    - The first column should be an "ID" column (INTEGER, PRIMARY KEY).  
-    - Provide appropriate SQL data types (VARCHAR(100) for text, INT for numbers).  
-    - Use proper SQL syntax. No explanations.  
-
-    Example Output:
-    ```
-    CREATE TABLE demo (
-        ID INT PRIMARY KEY, 
-        Name VARCHAR(100), 
-        Age INT
-    );
-    ```
-    """
-    schema_response = client.text_generation(schema_prompt, max_new_tokens=200).strip()
-
-    # Validate schema using sqlparse
-    parsed_schema = sqlparse.parse(schema_response)
-    if not parsed_schema:
-        return "Error: Could not generate a valid table schema.", None
-
-    # Extract table schema
-    table_schema = schema_response.replace("CREATE TABLE demo (", "").replace(");", "").strip()
-    
-    # Connect to MySQL and create the table dynamically
-    connection = mysql.connector.connect(host=db_host, user=db_user, password=db_pass, database=db_name)
-    cursor = connection.cursor()
-    cursor.execute(f"CREATE TABLE IF NOT EXISTS demo ({table_schema})")
-
-    # Generate demo data using LLM
-    data_prompt = f"""
-    Generate {num_rows} rows of structured demo data for this table schema:
-    
-    ```
-    {schema_response}
-    ```
-    
-    **Output Format:**  
-    - Return valid SQL INSERT statements.  
-    - Ensure all values match their respective column types.  
-    - Use double quotes ("") for text values.  
-    - No explanations, just raw SQL.
-
-    Example Output:
-    ```
-    INSERT INTO demo VALUES (1, "John Doe", 25);
-    INSERT INTO demo VALUES (2, "Jane Smith", 30);
-    ```
-    """
-    data_response = client.text_generation(data_prompt, max_new_tokens=1000).strip()
-
-    # Extract SQL INSERT statements using a better regex
-    insert_statements = re.findall(r'INSERT INTO demo VALUES \([^)]+\);', data_response, re.DOTALL)
-    if not insert_statements:
-        return "Error: Could not generate valid data.", None
-
-    # Insert data into the database
-    for statement in insert_statements:
-        cursor.execute(statement)
-    
-    connection.commit()
-    connection.close()
-    
-    return "Demo data inserted into the database successfully.", None
-def generate_demo_data_csv(user_input, num_rows=10):
-    """Generates realistic demo data using the LLM in valid CSV format."""
-    
-    prompt = f"""
-    Generate a structured dataset with {num_rows} rows based on the following request:
-    
-    "{user_input}"
-    
-    **Output Format:**  
-    - Ensure the response is in **valid CSV format** (comma-separated).  
-    - The **first row** must be column headers.  
-    - Use **double quotes for text values** to avoid formatting issues.  
-    - Do **not** include explanations—just the raw CSV data.
-
-    Example Output:
-    
-"ID","Name","Age","Email"
-    "1","John Doe","25","john.doe@example.com"
-    "2","Jane Smith","30","jane.smith@example.com"
-
-    """
-
-    # Get LLM response
-    response = client.text_generation(prompt, max_new_tokens=10000).strip()
-
-    # Ensure we extract only the CSV part (some models may add explanations)
-    csv_start = response.find('"ID"')  # Find where the CSV starts
-    if csv_start != -1:
-        response = response[csv_start:]  # Remove anything before the CSV
-
-    # Convert to DataFrame
-    try:
-        df = pd.read_csv(io.StringIO(response))  # Read as CSV
-    except Exception as e:
-        return f"Error: Invalid CSV format. {str(e)}", None
-    
-    # Save to a CSV file
-    file_path = "generated_data.csv"
-    df.to_csv(file_path, index=False)
-    
-    return "Demo data generated as CSV.", file_path  # Return file path
-
-def analyze_data(user_input):
-    """Analyzes data using Mistral."""
-    prompt = f"Analyze this data: {user_input}"
-    return client.text_generation(prompt, max_new_tokens=200).strip()
-
-def sql_chatbot(user_input, db_user=None, db_pass=None, db_host="localhost", db_name=None, num_rows=10):
-    task = classify_task(user_input)
-    
-    if "generate_sql" in task:
-        return generate_sql_query(user_input), None
-    
-    elif "create_table" in task:
-        return create_table(user_input, db_user, db_pass, db_host, db_name)
-    
-    elif "generate_demo_data_db" in task:
-        return generate_demo_data_db(user_input, db_user, db_pass, db_host, db_name, num_rows)
-    
-    elif "generate_demo_data_csv" in task:
-        response, file_path = generate_demo_data_csv(user_input, num_rows)
-        return response, file_path
-    elif "analyze_data" in task:
-        return analyze_data(user_input), None
-    
-    return f"task:{task} \n I could not understand your request.", None
-
-iface = gr.Interface(
-    fn=sql_chatbot,
-    inputs=[
-        gr.Textbox(label="User Input"),
-        gr.Textbox(label="MySQL Username", interactive=True),
-        gr.Textbox(label="MySQL Password", interactive=True, type="password"),
-        gr.Textbox(label="MySQL Host", interactive=True, value="localhost"),
-        gr.Textbox(label="Database Name", interactive=True),
-        gr.Number(label="Number of Rows", interactive=True, value=10, precision=0)
-    ],
-    outputs=[gr.Textbox(label="Response"), gr.File(label="File Output")]
-)
-
-iface.launch()
-# print("hi")
+import gradio as gr
+from huggingface_hub import InferenceClient
+import pandas as pd
+import io
+import mysql.connector
+import os
+
+HF_API_KEY = os.getenv("HF_API_KEY")
+
+# Hugging Face API Key (Replace with your actual key)
+client = InferenceClient(model="mistralai/Mistral-7B-Instruct-v0.3", token=HF_API_KEY)
+
+def classify_task(user_input):
+    """Classifies user task using Mistral."""
+    prompt = f"""
+You are an AI assistant that classifies user requests related to SQL and databases.  
+Your task is to categorize the input into one of the following options:  
+
+- **generate_sql** → If the user asks to generate **SQL syntax**, like SELECT, INSERT, CREATE TABLE, etc.  
+- **create_table** → If the user explicitly wants to **create** a database/table on a local MySQL server.  
+- **generate_demo_data_db** → If the user wants to insert demo data into a database.  
+- **generate_demo_data_csv** → If the user wants to generate demo data in CSV format.  
+- **analyze_data** → If the user asks for insights, trends, or statistical analysis of data.  
+
+**Examples:**
+1. "Give me SQL syntax to create a student table" → **generate_sql**  
+2. "Create a student table in my database" → **create_table**  
+3. "Insert some demo data in my database" → **generate_demo_data_db**  
+4. "Generate sample student data in CSV format" → **generate_demo_data_csv**  
+5. "Analyze student marks and trends" → **analyze_data**  
+
+**User Input:** {user_input}  
+
+**Output Format:** Return only the category name without any explanations.
+"""
+
+    response = client.text_generation(prompt, max_new_tokens=20).strip()
+    return response
+def generate_sql_query(user_input):
+    """Generates SQL queries using Mistral."""
+    prompt = f"Generate SQL syntax for: {user_input}"
+    return client.text_generation(prompt, max_new_tokens=200).strip()
+def generate_sql_query_for_create(user_input):
+    """Generates SQL queries using Mistral."""
+    prompt = f"""
+    Generate **only** the SQL syntax for: {user_input}
+    
+    **Rules:**
+    - No explanations, no bullet points, no extra text.
+    - Return **only valid SQL**.
+    
+    **Example Input:**  
+    "Create a student table with name, age, and email."
+    
+    **Example Output:**  
+    ```sql
+    CREATE TABLE student (
+        student_id INT PRIMARY KEY AUTO_INCREMENT,
+        name VARCHAR(100) NOT NULL,
+        age INT NOT NULL,
+        email VARCHAR(255) UNIQUE NOT NULL
+    );
+    ```
+    """
+
+    response = client.text_generation(prompt, max_new_tokens=200).strip()
+
+    # Remove unnecessary text (if any)
+    if "```sql" in response:
+        response = response.split("```sql")[1].split("```")[0].strip()
+
+    return response
+
+import pymysql
+
+def create_table(user_input, db_user, db_pass, db_host, db_name):
+    try:
+        # Validate inputs
+        if not all([db_user, db_pass, db_host, db_name, user_input]):
+            return "Please provide all required inputs (database credentials and table structure).", None
+
+        # Generate SQL schema using Mistral
+        
+        schema_response = generate_sql_query_for_create(user_input)
+        print(schema_response)
+        # Validate schema using sqlparse
+        parsed_schema = sqlparse.parse(schema_response)
+        if not parsed_schema:
+            return "Error: Could not generate a valid table schema.", None
+
+        # Connect to MySQL Server
+        connection = pymysql.connect(host=db_host, user=db_user, password=db_pass)
+        cursor = connection.cursor()
+
+        # Create Database if it doesn't exist
+        cursor.execute(f"CREATE DATABASE IF NOT EXISTS {db_name}")
+        connection.commit()
+        connection.close()
+
+        # Connect to the specified database
+        connection = pymysql.connect(host=db_host, user=db_user, password=db_pass, database=db_name)
+        cursor = connection.cursor()
+
+        # Execute the generated CREATE TABLE statement
+        cursor.execute(schema_response)
+        connection.commit()
+
+        return "Table created successfully.", None
+    
+    except pymysql.MySQLError as err:
+        return f"Error: {err}", None
+    
+    finally:
+        if 'connection' in locals() and connection.open:
+            cursor.close()
+            connection.close()
+
+import mysql.connector
+import re
+import sqlparse  # Install via: pip install sqlparse
+
+def generate_demo_data_db(user_input, db_user, db_pass, db_host, db_name, num_rows=10):
+    """Generates and inserts structured demo data into a database using LLM."""
+    
+    if not all([db_user, db_pass, db_name]):
+        return "Please provide database credentials.", None
+    
+    # Generate column definitions using LLM
+    schema_prompt = f"""
+    Extract column names and types from the following request:
+    
+    "{user_input}"
+    
+    **Output Format:**  
+    - The first column should be an "ID" column (INTEGER, PRIMARY KEY).  
+    - Provide appropriate SQL data types (VARCHAR(100) for text, INT for numbers).  
+    - Use proper SQL syntax. No explanations.  
+
+    Example Output:
+    ```
+    CREATE TABLE demo (
+        ID INT PRIMARY KEY, 
+        Name VARCHAR(100), 
+        Age INT
+    );
+    ```
+    """
+    schema_response = client.text_generation(schema_prompt, max_new_tokens=200).strip()
+
+    # Validate schema using sqlparse
+    parsed_schema = sqlparse.parse(schema_response)
+    if not parsed_schema:
+        return "Error: Could not generate a valid table schema.", None
+
+    # Extract table schema
+    table_schema = schema_response.replace("CREATE TABLE demo (", "").replace(");", "").strip()
+    
+    # Connect to MySQL and create the table dynamically
+    connection = mysql.connector.connect(host=db_host, user=db_user, password=db_pass, database=db_name)
+    cursor = connection.cursor()
+    cursor.execute(f"CREATE TABLE IF NOT EXISTS demo ({table_schema})")
+
+    # Generate demo data using LLM
+    data_prompt = f"""
+    Generate {num_rows} rows of structured demo data for this table schema:
+    
+    ```
+    {schema_response}
+    ```
+    
+    **Output Format:**  
+    - Return valid SQL INSERT statements.  
+    - Ensure all values match their respective column types.  
+    - Use double quotes ("") for text values.  
+    - No explanations, just raw SQL.
+
+    Example Output:
+    ```
+    INSERT INTO demo VALUES (1, "John Doe", 25);
+    INSERT INTO demo VALUES (2, "Jane Smith", 30);
+    ```
+    """
+    data_response = client.text_generation(data_prompt, max_new_tokens=1000).strip()
+
+    # Extract SQL INSERT statements using a better regex
+    insert_statements = re.findall(r'INSERT INTO demo VALUES \([^)]+\);', data_response, re.DOTALL)
+    if not insert_statements:
+        return "Error: Could not generate valid data.", None
+
+    # Insert data into the database
+    for statement in insert_statements:
+        cursor.execute(statement)
+    
+    connection.commit()
+    connection.close()
+    
+    return "Demo data inserted into the database successfully.", None
+def generate_demo_data_csv(user_input, num_rows=10):
+    """Generates realistic demo data using the LLM in valid CSV format."""
+    
+    prompt = f"""
+    Generate a structured dataset with {num_rows} rows based on the following request:
+    
+    "{user_input}"
+    
+    **Output Format:**  
+    - Ensure the response is in **valid CSV format** (comma-separated).  
+    - The **first row** must be column headers.  
+    - Use **double quotes for text values** to avoid formatting issues.  
+    - Do **not** include explanations—just the raw CSV data.
+
+    Example Output:
+    
+"ID","Name","Age","Email"
+    "1","John Doe","25","john.doe@example.com"
+    "2","Jane Smith","30","jane.smith@example.com"
+
+    """
+
+    # Get LLM response
+    response = client.text_generation(prompt, max_new_tokens=10000).strip()
+
+    # Ensure we extract only the CSV part (some models may add explanations)
+    csv_start = response.find('"ID"')  # Find where the CSV starts
+    if csv_start != -1:
+        response = response[csv_start:]  # Remove anything before the CSV
+
+    # Convert to DataFrame
+    try:
+        df = pd.read_csv(io.StringIO(response))  # Read as CSV
+    except Exception as e:
+        return f"Error: Invalid CSV format. {str(e)}", None
+    
+    # Save to a CSV file
+    file_path = "generated_data.csv"
+    df.to_csv(file_path, index=False)
+    
+    return "Demo data generated as CSV.", file_path  # Return file path
+
+def analyze_data(user_input):
+    """Analyzes data using Mistral."""
+    prompt = f"Analyze this data: {user_input}"
+    return client.text_generation(prompt, max_new_tokens=200).strip()
+
+def sql_chatbot(user_input, db_user=None, db_pass=None, db_host="localhost", db_name=None, num_rows=10):
+    task = classify_task(user_input)
+    
+    if "generate_sql" in task:
+        return generate_sql_query(user_input), None
+    
+    elif "create_table" in task:
+        return create_table(user_input, db_user, db_pass, db_host, db_name)
+    
+    elif "generate_demo_data_db" in task:
+        return generate_demo_data_db(user_input, db_user, db_pass, db_host, db_name, num_rows)
+    
+    elif "generate_demo_data_csv" in task:
+        response, file_path = generate_demo_data_csv(user_input, num_rows)
+        return response, file_path
+    elif "analyze_data" in task:
+        return analyze_data(user_input), None
+    
+    return f"task:{task} \n I could not understand your request.", None
+
+iface = gr.Interface(
+    fn=sql_chatbot,
+    inputs=[
+        gr.Textbox(label="User Input"),
+        gr.Textbox(label="MySQL Username", interactive=True),
+        gr.Textbox(label="MySQL Password", interactive=True, type="password"),
+        gr.Textbox(label="MySQL Host", interactive=True, value="localhost"),
+        gr.Textbox(label="Database Name", interactive=True),
+        gr.Number(label="Number of Rows", interactive=True, value=10, precision=0)
+    ],
+    outputs=[gr.Textbox(label="Response"), gr.File(label="File Output")]
+)
+
+iface.launch()
+# print("hi")
 # print(create_table("create a SQL student table","root", "123456", "localhost", "demo"))