Update app.py

app.py

@@ -1,10 +1,9 @@
-from smolagents import CodeAgent, HfApiModel, load_tool, tool
+from smolagents import CodeAgent, HfApiModel, tool
 import yaml
 import gradio as gr
 
 def classify_incident(service_criticity: str, disruption: str, affectation_time: str, magnitude: str, workaround: str) -> str:
     """Classifies an incident based on predefined rules."""
-    
     if service_criticity == "High" and disruption == "Full":
         return "P1"  # Critical issue
     elif service_criticity == "High" and (disruption == "Degraded" or affectation_time == "Up 15 mins"):
@@ -16,23 +15,15 @@ def classify_incident(service_criticity: str, disruption: str, affectation_time:
 
 @tool
 def diagnose_incident(service_criticity: str, disruption: str, affectation_time: str, magnitude: str, workaround: str) -> str:
-    """Diagnoses an incident and determines its severity priority.
-    Args:
-        service_criticity: High or Low
-        disruption: Full, Degraded, or None
-        affectation_time: Up 15 mins, Less 15 mins
-        magnitude: High, Low, or None
-        workaround: Yes or No
-    """
+    """Determines the severity priority of an incident."""
     priority = classify_incident(service_criticity, disruption, affectation_time, magnitude, workaround)
     return f"Incident classified as priority: {priority}"
 
-# Load model from Hugging Face
+# Load model with optimized settings
 model = HfApiModel(
-    max_tokens=2096,
-    temperature=0.5,
-    model_id='Qwen/Qwen2.5-Coder-32B-Instruct',
-    custom_role_conversions=None,
+    max_tokens=512,  # Reduce max tokens to improve response time
+    temperature=0.3,  # Lower temperature for more deterministic results
+    model_id='Qwen/Qwen2.5-Coder-32B-Instruct'
 )
 
 # Load prompt templates
@@ -43,40 +34,33 @@ with open("prompts.yaml", 'r') as stream:
 agent = CodeAgent(
     model=model,
     tools=[diagnose_incident],
-    max_steps=6,
-    verbosity_level=1,
-    grammar=None,
-    planning_interval=None,
+    max_steps=3,  # Reduce steps to minimize latency
+    verbosity_level=0,  # Lower verbosity for efficiency
     name="Incident Diagnosis Agent",
     description="An agent that classifies incidents based on severity criteria.",
     prompt_templates=prompt_templates
 )
 
-def chat_diagnose_incident(conversation):
-    """Simulates a conversation with the agent to diagnose an incident."""
-    
-    conversation.append("Agent: Hola, cuéntame qué ocurrió con el servicio.")
-    service_criticity = gr.Textbox(label="Tu respuesta")
-    conversation.append("Agent: ¿El problema causa una interrupción total o solo degradación del servicio?")
-    disruption = gr.Textbox(label="Tu respuesta")
-    conversation.append("Agent: ¿Cuánto tiempo lleva afectado?")
-    affectation_time = gr.Textbox(label="Tu respuesta")
-    conversation.append("Agent: ¿Qué tan grave es el impacto en el sistema?")
-    magnitude = gr.Textbox(label="Tu respuesta")
-    conversation.append("Agent: ¿Existe algún tipo de solución alternativa o workaround?")
-    workaround = gr.Textbox(label="Tu respuesta")
-    
+def chat_diagnose_incident(service_criticity, disruption, affectation_time, magnitude, workaround):
+    """Diagnoses an incident based on user inputs."""
     priority = diagnose_incident(service_criticity, disruption, affectation_time, magnitude, workaround)
-    conversation.append(f"Agent: Con base en la información, el incidente se clasifica como prioridad {priority}.")
-    return "\n".join(conversation)
+    return f"Agent: El incidente se clasifica como prioridad {priority}."
 
-# Gradio UI para conversación
-iface = gr.ChatInterface(
+# Gradio UI
+iface = gr.Interface(
     fn=chat_diagnose_incident,
+    inputs=[
+        gr.Dropdown(["High", "Low"], label="Criticidad del servicio"),
+        gr.Dropdown(["Full", "Degraded", "None"], label="Interrupción"),
+        gr.Dropdown(["Up 15 mins", "Less 15 mins"], label="Tiempo de afectación"),
+        gr.Dropdown(["High", "Low", "None"], label="Magnitud"),
+        gr.Dropdown(["Yes", "No"], label="¿Hay workaround?"),
+    ],
+    outputs=gr.Textbox(),
     title="Asistente de Diagnóstico de Incidentes",
-    description="Conversación interactiva con el agente para clasificar la severidad del incidente.",
-    theme="dark"
+    description="Selecciona las opciones para diagnosticar el incidente.",
+    theme="default"
 )
 
-# Lanzar UI
-iface.launch()
+# Lanzar UI\ if __name__ == "__main__":
+    iface.launch()