Test

app.py

@@ -1,10 +1,16 @@
-import gradio as gr
-import ollama
+import os
 import json
 
+import ollama
+import gradio
+
+
+OLLAMA_HOST = os.getenv('OLLAMA_HOST', "192.168.10.100:11434")
+
+
 json_code = "```json\n{\n    \"classes\": [\n        \"http://data.europa.eu/949/ContactLineSystem\"\n    ],\n    \"properties\": [\n        \"http://data.europa.eu/949/contactLineSystemType\",\n        \"http://data.europa.eu/949/energySupplySystemTSICompliant\",\n        \"http://data.europa.eu/949/conditionsAppliedRegenerativeBraking\",\n        \"http://data.europa.eu/949/conditionalRegenerativeBrake\"\n    ]\n}\n```"
 
-client_gpu_local = ollama.Client(host='http://192.168.10.100:11434')
+client_gpu_local = ollama.Client(host=f"http://{OLLAMA_HOST}", timeout=15)
 
 model = "test_class_prop"
 
@@ -24,27 +30,33 @@ def clean_output():
 def submit_query(msg):
     
     if msg != "":
-    
-        prompt = f"Identify the classes and properties used in this natural language query: \"{msg}\""
-        
-        messages = [{'role': 'user', 'content': prompt}]
         
-        response = client_gpu_local.chat(
-            model,
-            messages=messages,
-            options=model_options
-        )
+        try:
+    
+            prompt = f"Identify the classes and properties used in this natural language query: \"{msg}\""
+            
+            messages = [{'role': 'user', 'content': prompt}]
+            
+            response = client_gpu_local.chat(
+                model,
+                messages=messages,
+                options=model_options
+            )
 
-        msg = response.message.content
+            msg = response.message.content
 
-        try:
+            try:
 
-            json_data_test = json.loads(msg.split("```json")[1].split("```")[0])
+                json_data_test = json.loads(msg.split("```json")[1].split("```")[0])
+                
+                return json.dumps(json_data_test, indent=2)
+            
+            except:
+                return "{}"
             
-            return json.dumps(json_data_test, indent=2)
-        
         except:
-            return "{}"
+            
+            raise gradio.Error("Error: LLM request timed out!", duration=5)
     
     return "{}"
         
@@ -98,32 +110,32 @@ examples_data = [[e["instruction"].split(": ")[1], e["output"].split("```json")[
     
     
 
-with gr.Blocks() as demo:
+with gradio.Blocks() as demo:
     
-    query = gr.Textbox(render=False, label="Query", placeholder="Write a query and press Enter.", submit_btn="Send query")
+    query = gradio.Textbox(render=False, label="Query", placeholder="Write a query and press Enter.", submit_btn="Send query")
    
     
-    code_llm = gr.Code(render=False, label="LLM output", interactive=False, language="json")
-    code_ref = gr.Code(render=False, label="Expected output", interactive=False, language="json")
+    code_llm = gradio.Code(render=False, label="LLM output", interactive=False, language="json")
+    code_ref = gradio.Code(render=False, label="Expected output", interactive=False, language="json")
         
-    #chat = gr.Chatbot(render=False, value=history, label="LLM output", type="messages")
+    #chat = gradio.Chatbot(render=False, value=history, label="LLM output", type="messages")
     
-    with gr.Row():
+    with gradio.Row():
         query.render()
             
-    with gr.Row():
+    with gradio.Row():
         
-        with gr.Accordion(label="Examples", open=False):
+        with gradio.Accordion(label="Examples", open=False):
             
-            gr.Examples(label="Query examples", examples=examples_data, example_labels=[e[0] for e in examples_data], cache_examples=False, inputs=[query, code_ref],examples_per_page=10)
+            gradio.Examples(label="Query examples", examples=examples_data, example_labels=[e[0] for e in examples_data], cache_examples=False, inputs=[query, code_ref],examples_per_page=10)
     
             code_ref.render()
-    #with gr.Row():
+    #with gradio.Row():
         #chat.render()
         
 
         
-    with gr.Row():
+    with gradio.Row():
         code_llm.render()
          
     query.submit(submit_query,  inputs=[query], outputs=[code_llm])