startup

app.py

@@ -2,13 +2,12 @@ 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|>
+ALPACA_TEMPLATE = """<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a helpful assistant. You are an expert at Postgresql and SQL and psql. <|eot_id|><|start_header_id|>user<|end_header_id|>
 
-### Instruction: Write a PostGIS SQL statement for the following.
+### Instruction: Write a SQL statement for the following.
 {instruction}
 
 ### Input:
@@ -18,23 +17,22 @@ ALPACA_TEMPLATE = """<|begin_of_text|><|start_header_id|>system<|end_header_id|>
 <|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)'"
+    "Example one here",
+    "Example two here",
+    "Example three here",
 ]
 
-'''@spaces.GPU
+
+@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)
+    # Modern format for Gradio 5/6
     chat_history.append({"role": "user", "content": user_message})
     chat_history.append({"role": "assistant", "content": None})
 
@@ -55,7 +53,7 @@ def respond(user_message, chat_history):
     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")
 
@@ -80,20 +78,11 @@ with gr.Blocks(title="Text to SQL via Llama 3.2") as demo:
     gr.Examples(
         examples=PRESET_EXAMPLES,
         inputs=msg,
-        label="Click an example → then Submit"
+        label="Click an example → then click Submit"
     )
 
-    submit_btn.click(
-        fn=respond,
-        inputs=[msg, chatbot],
-        outputs=[chatbot, msg]
-    )
-
-    msg.submit(
-        fn=respond,
-        inputs=[msg, chatbot],
-        outputs=[chatbot, 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])
@@ -102,82 +91,36 @@ with gr.Blocks(title="Text to SQL via Llama 3.2") as demo:
 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",
-    )
+'''from transformers import pipeline
+import gradio as gr
+import spaces
 
-    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")
+# 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")
 
-    gr.Markdown("### Quick Examples (click to fill, then Submit)")
-    gr.Examples(
-        examples=PRESET_EXAMPLES,
-        inputs=msg,
-    )
+# 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|>
 
-    submit_btn.click(
-        fn=respond,
-        inputs=[msg, chatbot],
-        outputs=[chatbot, msg]
-    )
+### Instruction: Write a PostGIS SQL statement for the following.
+{instruction}
 
-    msg.submit(
-        fn=respond,
-        inputs=[msg, chatbot],
-        outputs=[chatbot, msg]
-    )
+### Input:
+{input}
 
-    clear_btn = gr.Button("Clear Chat")
-    clear_btn.click(lambda: ([], ""), outputs=[chatbot, msg])
+### 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)'"
+]
 
-if __name__ == "__main__":
-    print("Gradio version:", gr.__version__)
-    demo.launch()'''
 
 @spaces.GPU
 def chat_predict(message, history):
@@ -210,3 +153,4 @@ demo = gr.ChatInterface(
 if __name__ == "__main__":
     print("gradio " + gr.__version__)
     demo.launch()
+'''