Update app.py

app.py

@@ -1,64 +1,174 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
+# 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")
+# """
+# 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}]
+# 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]})
+#     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})
+#     messages.append({"role": "user", "content": message})
 
-    response = ""
+#     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
+#     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
+#         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}
 """
-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)",
-        ),
-    ],
+
+# 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)
 
-if __name__ == "__main__":
-    demo.launch()