startup

app.py

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+from transformers import pipeline
+import gradio as gr
+import spaces
+
+# Initialize the pipeline with an Alpaca-tuned model
+pipe = pipeline("text-generation", model="markrodrigo/Llama-3.2-3B-Instruct-Spatial-SQL-1.1", device_map="auto")
+
+# The Alpaca instruction prompt format
+ALPACA_TEMPLATE = """<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a helpful assistant. You are an expert at PostGIS and Postgresql and SQL and psql. <|eot_id|><|start_header_id|>user<|end_header_id|>
+
+### Instruction: Write a PostGIS SQL statement for the following.
+{instruction}
+
+### Input:
+{input}
+
+### Response:
+<|eot_id|><|start_header_id|>assistant<|end_header_id|>
+"""
+
+# Define your list of pre-set example prompts
+PRESET_EXAMPLES = [
+    "What is the area for the polygon? : 'Polygon ((-3.7515154 40.3855551, -3.7514972 40.3856581, -3.7507005 40.3855767, -3.7507167 40.3854722, -3.7515154 40.3855551))'",
+    "What is the centroid for the polygon? : 'Polygon ((-3.6934636 40.4808785, -3.6933352 40.4811486, -3.6930125 40.4810598, -3.693141 40.4807897, -3.6934636 40.4808785))'",
+    "What is the thousand meter buffer for the following point? : 'Point(-8.7522658 41.3862664)'",
+    "How long is the line? : 'LINESTRING (-3.6976693 40.4263178, -3.6986082 40.4258729)'",
+    "How far apart is the point and line? : 'Point(-109.87549823 38.60574249)' 'LineString(-109.24324628 38.76349931, -109.4821773 38.6875815)'"
+]
+
+@spaces.GPU
+def respond(user_message, chat_history):
+    chat_history = chat_history or []
+
+    if not user_message or not user_message.strip():
+        return chat_history, ""
+
+    # ✅ Modern messages format (required in Gradio 5/6)
+    chat_history.append({"role": "user", "content": user_message})
+    chat_history.append({"role": "assistant", "content": None})
+
+    prompt = ALPACA_TEMPLATE.format(instruction=user_message, input="")
+
+    sequences = pipe(
+        prompt,
+        max_new_tokens=256,
+        return_full_text=False,
+        temperature=0.4,
+        top_k=100,
+        do_sample=True,
+    )
+
+    bot_response = sequences[0]["generated_text"].strip()
+    chat_history[-1]["content"] = bot_response
+
+    return chat_history, ""
+
+
+# ====================== UI ======================
+with gr.Blocks(title="Text to SQL via Llama 3.2") as demo:
+    gr.Markdown("# Text to SQL via Llama 3.2\n### Convert natural language to PostgreSQL queries")
+
+    chatbot = gr.Chatbot(
+        label="Chat",
+        height=500,
+        type="messages",
+        show_copy_button=True,
+    )
+
+    with gr.Row():
+        with gr.Column(scale=5):
+            msg = gr.Textbox(
+                placeholder="Describe what SQL query you need...",
+                lines=2,
+                container=False
+            )
+        with gr.Column(scale=1, min_width=100):
+            submit_btn = gr.Button("Submit", variant="primary")
+
+    gr.Markdown("### Quick Examples")
+    gr.Examples(
+        examples=PRESET_EXAMPLES,
+        inputs=msg,
+        label="Click an example → then Submit"
+    )
+
+    submit_btn.click(
+        fn=respond,
+        inputs=[msg, chatbot],
+        outputs=[chatbot, msg]
+    )
+
+    msg.submit(
+        fn=respond,
+        inputs=[msg, chatbot],
+        outputs=[chatbot, msg]
+    )
+
+    clear_btn = gr.Button("Clear Chat")
+    clear_btn.click(lambda: ([], ""), outputs=[chatbot, msg])
+
+
+if __name__ == "__main__":
+    print("Gradio version:", gr.__version__)
+    demo.launch()
+
+# gradio < 5
+'''@spaces.GPU
+def respond(user_message, chat_history):
+    chat_history = chat_history or []
+
+    if not user_message or not user_message.strip():
+        return chat_history, ""
+
+    # Modern messages format (required in Gradio 5/6)
+    chat_history.append({"role": "user", "content": user_message})
+    chat_history.append({"role": "assistant", "content": None})
+
+    prompt = ALPACA_TEMPLATE.format(instruction=user_message, input="")
+
+    sequences = pipe(
+        prompt,
+        max_new_tokens=256,
+        return_full_text=False,
+        temperature=0.4,
+        top_k=100,
+        do_sample=True,
+    )
+
+    bot_response = sequences[0]["generated_text"].strip()
+    chat_history[-1]["content"] = bot_response
+
+    return chat_history, ""
+
+
+# ====================== UI ======================
+with gr.Blocks(title="Text to SQL via Llama 3.2") as demo:
+    gr.Markdown("# Text to SQL via Llama 3.2\n### Convert natural language to PostgreSQL queries")
+
+    chatbot = gr.Chatbot(
+        label="Chat",
+        height=500,
+        type="messages",
+    )
+
+    with gr.Row():
+        with gr.Column(scale=5):
+            msg = gr.Textbox(
+                placeholder="Describe what SQL query you need...",
+                lines=2,
+                container=False
+            )
+        with gr.Column(scale=1, min_width=100):
+            submit_btn = gr.Button("Submit", variant="primary")
+
+    gr.Markdown("### Quick Examples (click to fill, then Submit)")
+    gr.Examples(
+        examples=PRESET_EXAMPLES,
+        inputs=msg,
+    )
+
+    submit_btn.click(
+        fn=respond,
+        inputs=[msg, chatbot],
+        outputs=[chatbot, msg]
+    )
+
+    msg.submit(
+        fn=respond,
+        inputs=[msg, chatbot],
+        outputs=[chatbot, msg]
+    )
+
+    clear_btn = gr.Button("Clear Chat")
+    clear_btn.click(lambda: ([], ""), outputs=[chatbot, msg])
+
+
+if __name__ == "__main__":
+    print("Gradio version:", gr.__version__)
+    demo.launch()'''
+
+'''@spaces.GPU
+def chat_predict(message, history):
+    # For a chat interface, we treat the latest user message as the instruction
+    # and leave the input field empty for this example.
+    prompt = ALPACA_TEMPLATE.format(instruction=message, input="")
+    
+    # Generate text (adjust max_new_tokens as needed)
+    sequences = pipe(
+        prompt,
+        max_new_tokens=128,
+        return_full_text=False,
+        temperature=0.4,
+        top_k=100,
+        # top_p=0.9,
+    )
+    
+    # Extract and return the generated text
+    response = sequences[0]['generated_text'].strip()
+    return response
+
+# Create the Gradio ChatInterface
+demo = gr.ChatInterface(
+    fn=chat_predict,
+    title="Text to PostGIS SQL via Llama 3.2 - Primary Functions",
+    description="LLama 3.2 Spatial - Text to PostGIS SQL",
+)
+
+if __name__ == "__main__":
+    print("gradio " + gr.__version__)
+    demo.launch()'''

requirements.txt

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+torch
+transformers
+gradio>=6.0
+accelerate
+spaces