Update app.py

app.py

@@ -1,69 +1,45 @@
-from smolagents import CodeAgent, HfApiModel, load_tool, tool
+from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel, load_tool, tool
 import datetime
+import requests
 import pytz
 import yaml
-from transformers import pipeline
 from tools.final_answer import FinalAnswerTool
 from Gradio_UI import GradioUI
 
-# Initialize ASR model from Hugging Face
-asr_pipeline = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-large-960h")
-
-# Tool: Convert time between time zones
+# Herramienta para clasificar alertas P1, P2, P3 basado en criterios de la tabla
 @tool
-def convert_time(time_str: str, from_tz: str, to_tz: str) -> str:
-    """Convert time from one timezone to another.
+def classify_alert(service_criticity: str, disruption: str, affectation_time: str, magnitude: str, business_workaround: str) -> str:
+    """Clasifica una alerta en P1, P2 o P3 según los criterios especificados.
     Args:
-        time_str: Time in 'YYYY-MM-DD HH:MM:SS' format.
-        from_tz: Original timezone.
-        to_tz: Target timezone.
+        service_criticity: 'High' o 'Low'
+        disruption: 'Full', 'Degraded', 'None'
+        affectation_time: 'Up 15 mins' o 'Less 15 mins'
+        magnitude: 'High', 'Low', 'None'
+        business_workaround: 'Yes' o 'No'
     """
-    try:
-        from_zone = pytz.timezone(from_tz)
-        to_zone = pytz.timezone(to_tz)
-        local_time = datetime.datetime.strptime(time_str, "%Y-%m-%d %H:%M:%S")
-        local_time = from_zone.localize(local_time)
-        converted_time = local_time.astimezone(to_zone).strftime("%Y-%m-%d %H:%M:%S")
-        return f"Time in {to_tz}: {converted_time}"
-    except Exception as e:
-        return f"Error converting time: {str(e)}"
-
+    if service_criticity == "High" and disruption in ["Full", "Degraded"] and magnitude == "High" and business_workaround == "No":
+        return "P1 - Crítico"
+    elif service_criticity == "High" and (disruption in ["Full", "Degraded"] or affectation_time == "Up 15 mins"):
+        return "P2 - Importante"
+    else:
+        return "P3 - Menor"
 
-# Tool: Assign alerts to departments based on keywords
+# Herramienta para generar resúmenes de reportes de infraestructura
 @tool
-def assign_alert(alert_message: str) -> str:
-    """Assign an alert to a department based on keywords.
+def summarize_report(report_text: str) -> str:
+    """Genera un resumen de un reporte sobre la infraestructura de la plataforma.
     Args:
-        alert_message: The alert description.
+        report_text: Texto del reporte.
     """
-    alert_keywords = {
-        "network": "Network Operations",
-        "server": "Infrastructure",
-        "database": "Database Team",
-        "security": "Security Operations",
-        "application": "Development Team"
-    }
-    for keyword, department in alert_keywords.items():
-        if keyword.lower() in alert_message.lower():
-            return f"Alert assigned to: {department}"
-    return "Alert could not be assigned automatically. Please check manually."
-
-
-# Tool: Transcribe audio and generate a timeline using Hugging Face ASR
-@tool
-def transcribe_audio(audio_path: str) -> str:
-    """Transcribe audio and log events with timestamps.
-    Args:
-        audio_path: Path to the audio file.
-    """
-    try:
-        result = asr_pipeline(audio_path)
-        transcript = result["text"]
-        timestamped_events = [f"{datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')} - {line}" for line in transcript.split('.')]
-        return "\n".join(timestamped_events)
-    except Exception as e:
-        return f"Error transcribing audio: {str(e)}"
-
+    api_url = "https://api-inference.huggingface.co/models/facebook/bart-large-cnn"
+    headers = {"Authorization": "Bearer YOUR_HF_API_KEY"}
+    payload = {"inputs": report_text}
+    
+    response = requests.post(api_url, headers=headers, json=payload)
+    if response.status_code == 200:
+        return response.json()[0]['summary_text']
+    else:
+        return "Error al generar el resumen."
 
 final_answer = FinalAnswerTool()
 
@@ -74,19 +50,22 @@ model = HfApiModel(
     custom_role_conversions=None,
 )
 
+image_generation_tool = load_tool("agents-course/text-to-image", trust_remote_code=True)
+
 with open("prompts.yaml", 'r') as stream:
     prompt_templates = yaml.safe_load(stream)
-
+    
 agent = CodeAgent(
     model=model,
-    tools=[final_answer, convert_time, assign_alert, transcribe_audio],
+    tools=[final_answer, classify_alert, summarize_report],
     max_steps=6,
     verbosity_level=1,
     grammar=None,
     planning_interval=None,
-    name="Monitoring Assistant",
-    description="An agent that helps with alert assignment, time conversion, monitoring tasks, and real-time transcription.",
+    name=None,
+    description=None,
     prompt_templates=prompt_templates
 )
 
 GradioUI(agent).launch()
+