app.py

import gradio as gr
# from huggingface_hub import InferenceClient

# """
# For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
# """
# client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")


# def respond(
#     message,
#     history: list[tuple[str, str]],
#     system_message,
#     max_tokens,
#     temperature,
#     top_p,
# ):
#     messages = [{"role": "system", "content": system_message}]

#     for val in history:
#         if val[0]:
#             messages.append({"role": "user", "content": val[0]})
#         if val[1]:
#             messages.append({"role": "assistant", "content": val[1]})

#     messages.append({"role": "user", "content": message})

#     response = ""

#     for message in client.chat_completion(
#         messages,
#         max_tokens=max_tokens,
#         stream=True,
#         temperature=temperature,
#         top_p=top_p,
#     ):
#         token = message.choices[0].delta.content

#         response += token
#         yield response


# """
# For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
# """
# demo = gr.ChatInterface(
#     respond,
#     additional_inputs=[
#         gr.Textbox(value="You are a friendly SQL Chatbot.", label="System message"),
#         gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
#         gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
#         gr.Slider(
#             minimum=0.1,
#             maximum=1.0,
#             value=0.95,
#             step=0.05,
#             label="Top-p (nucleus sampling)",
#         ),
#     ],
# )


# if __name__ == "__main__":
#     demo.launch()

# Import necessary libraries
import gradio as gr

# Define the prompt template
odoo_text2sql_prompt = """
Instruction: {instruction}
Input: {input_text}
Output: {output_text}
DB Schema: {db_schema}
"""

# Define the database schema
db_schema = """
CREATE TABLE product_product (
    id SERIAL NOT NULL,
    message_main_attachment_id INTEGER,
    product_tmpl_id INTEGER NOT NULL,
    create_uid INTEGER,
    write_uid INTEGER,
    default_code VARCHAR,
    barcode VARCHAR,
    combination_indices VARCHAR,
    volume NUMERIC,
    weight NUMERIC,
    active BOOLEAN,
    can_image_variant_1024_be_zoomed BOOLEAN,
    create_date TIMESTAMP WITHOUT TIME ZONE,
    write_date TIMESTAMP WITHOUT TIME ZONE,
    store_qty_available DOUBLE PRECISION,
    store_standard_price DOUBLE PRECISION,
    store_sales_count DOUBLE PRECISION,
    CONSTRAINT product_product_pkey PRIMARY KEY (id),
    CONSTRAINT product_product_create_uid_fkey FOREIGN KEY(create_uid) REFERENCES res_users (id) ON DELETE SET NULL,
    CONSTRAINT product_product_message_main_attachment_id_fkey FOREIGN KEY(message_main_attachment_id) REFERENCES ir_attachment (id) ON DELETE SET NULL,
    CONSTRAINT product_product_product_tmpl_id_fkey FOREIGN KEY(product_tmpl_id) REFERENCES product_template (id) ON DELETE CASCADE,
    CONSTRAINT product_product_write_uid_fkey FOREIGN KEY(write_uid) REFERENCES res_users (id) ON DELETE SET NULL
)
"""

# Function to generate SQL query (placeholder function)
def generate_sql(instruction, input_text):
    return "Model is not loaded. Please ensure you have the necessary GPU resources."

# Function to clear inputs
def clear_inputs():
    return "", ""

# Create the Gradio interface with enhanced features
with gr.Blocks(css="""
    .centered {
        display: flex;
        justify-content: center;
        align-items: center;
        text-align: center;
    }
    .title {
        font-size: 2em;
        font-weight: bold;
        margin-bottom: 20px;
    }
    .description {
        font-size: 1.2em;
        margin-bottom: 20px;
    }
    .button {
        background-color: #007BFF; /* Sea blue color */
        color: white;
        border: none;
        padding: 10px 20px;
        text-align: center;
        text-decoration: none;
        display: inline-block;
        font-size: 16px;
        margin: 4px 2px;
        cursor: pointer;
        border-radius: 12px;
    }
    .button:hover {
        background-color: #0056b3;
    }
""") as demo:
    gr.Markdown('<div class="centered"><div class="title">DeepSQL AI Assistant</div></div>')
    gr.Markdown('<div class="centered"><div class="description">Generate SQL queries for Database Schema based on Natural Language input.</div></div>')

    with gr.Row():
        with gr.Column():
            instruction = gr.Textbox(lines=7, placeholder="Enter the instruction here...", label="Instruction")
            input_text = gr.Textbox(lines=7, placeholder="Enter the input text here...", label="Input Text")
            clear_button = gr.Button("Clear", elem_classes="button")

        with gr.Column():
            output = gr.Textbox(lines=15, placeholder="Generated SQL query will appear here...", label="Output SQL Query")
            feedback = gr.Textbox(lines=2, placeholder="Provide your feedback here...", label="Feedback")

    examples = gr.Examples(
        examples=[
            ["Find the top 5 products with the highest sales count.", "What are the top sales products?"],
            ["List all active products.", "Show me all active products."],
        ],
        inputs=[instruction, input_text]
    )

    submit_button = gr.Button("Generate SQL", elem_classes="button")
    submit_button.click(generate_sql, inputs=[instruction, input_text], outputs=output)
    clear_button.click(clear_inputs, outputs=[instruction, input_text])

# Launch the Gradio interface with sharing enabled
demo.launch(share=True)