Hugging Face's logo

Spaces:
imsanjoykb
/
deepSQL-R1-distill-8B
Sleeping

App Files Community
deepSQL-R1-distill-8B
File size: 4,603 Bytes 
23a1154
9978b1a
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
32a2d3e
82535c8
 
 
 
 
 
 
23a1154
82535c8
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
23a1154
82535c8
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3d8da73
82535c8
a199a4e
32a2d3e
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
 
import gradio as gr
# from unsloth import FastLanguageModel
# import torch
# from transformers import TextStreamer

# # Define the model name and other parameters
# model_name = "imsanjoykb/deepSQL-R1-distill-8B"
# max_seq_length = 2048
# dtype = None
# load_in_4bit = True

# # Initialize model and tokenizer to None
# model = None
# tokenizer = None

# # Try to load the model and tokenizer from Hugging Face
# try:
#     model, tokenizer = FastLanguageModel.from_pretrained(
#         model_name=model_name,
#         max_seq_length=max_seq_length,
#         dtype=dtype,
#         load_in_4bit=load_in_4bit,
#     )
#     # Enable faster inference
#     FastLanguageModel.for_inference(model)
# except Exception as e:
#     print(f"Failed to load model: {e}")


# 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()