Update agent.py

agent.py

@@ -1,52 +1,101 @@
+from smolagents import Tool, DuckDuckGoSearchTool, GoogleSearchTool, VisitWebpageTool, WikipediaSearchTool, PythonInterpreterTool, FinalAnswerTool
+from tavily import TavilyClient
+from dotenv import load_dotenv
 import os
-import requests
 
-class DuckDuckGoAgent:
+load_dotenv()  # Cargar variables de entorno desde .env
+
+# Inicializar herramientas directamente en agent.py
+duck_search = DuckDuckGoSearchTool()
+google_search = GoogleSearchTool()
+visit_page = VisitWebpageTool()
+wiki_search = WikipediaSearchTool()
+do_python = PythonInterpreterTool()
+final_answer = FinalAnswerTool()
+tavily_search = TavilyClient()
+
+# Herramienta de transcripción de audio a texto
+speech_to_text_tool = Tool.from_space("hf-audio/whisper-large-v3-turbo", 
+                                      name="speech_to_text_tool", 
+                                      description="""Convierte un archivo de audio a texto. Usa este comando: 
+                                                     'speech_to_text_tool(filename)'""", 
+                                      api_name="/predict")
+
+# Herramienta de QA visual
+visual_qa_tool = Tool.from_space("sitammeur/PicQ", 
+                                 name="visual_qa_tool", 
+                                 description="""Responde preguntas sobre una imagen proporcionada.
+                                                Usa el comando: visual_qa_tool(question=<pregunta>, image=<nombre_de_imagen>)""",
+                                 api_name="/predict")
+
+# Inicializar el modelo de Azure OpenAI
+from smolagents import AzureOpenAIServerModel
+import app_tokens
+
+model = AzureOpenAIServerModel(
+    model_id=app_tokens.AZURE_OPENAI_MODEL,
+    azure_endpoint=app_tokens.AZURE_OPENAI_ENDPOINT,
+    api_key=app_tokens.AZURE_OPENAI_API_KEY,
+    api_version=app_tokens.OPENAI_API_VERSION
+)
+
+class BasicAgent:
     def __init__(self):
-        print("DuckDuckGoAgent initialized.")
-        self.headers = {"User-Agent": "Mozilla/5.0"}
-
-        self.model_config = {
-            "api_key": os.getenv("HF_API_TOKEN"),
-            "model_name": "mistralai/Mistral-7B-Instruct-v0.1"
-        }
-
-    def __call__(self, question: str) -> str:
-        print(f"Agent received question: {question[:50]}...")
-        answer = self.call_model_backend(question)
-        print(f"Agent returning answer: {answer}")
-        return answer.strip()
-
-    def call_model_backend(self, prompt: str) -> str:
-        api_key = self.model_config.get("api_key")
-        model = self.model_config.get("model_name")
-
-        if not api_key or not model:
-            return "Error: Hugging Face API Token or model not configured."
-
-        url = f"https://api-inference.huggingface.co/models/{model}"
-        headers = {
-            "Authorization": f"Bearer {api_key}",
-            "Content-Type": "application/json"
-        }
-        payload = {
-            "inputs": prompt,
-            "parameters": {
-                "max_new_tokens": 200,
-                "temperature": 0.7
-            }
-        }
-
-        try:
-            response = requests.post(url, headers=headers, json=payload, timeout=30)
-            response.raise_for_status()
-            output = response.json()
-            if isinstance(output, list) and "generated_text" in output[0]:
-                return output[0]["generated_text"].strip()[:200]
-            elif isinstance(output, dict) and "error" in output:
-                return f"HF LLM error: {output['error']}"
-            else:
-                return "No response generated from Hugging Face LLM."
-        except Exception as e:
-            print(f"Error contacting Hugging Face LLM: {e}")
-            return "Error contacting Hugging Face model."
+        # Agente de búsqueda en la web
+        self.web_agent = CodeAgent(
+            model=model,
+            tools=[visit_page, wiki_search, google_search, final_answer],
+            max_steps=8,
+            name="web_agent",
+            description="Este agente realiza búsquedas en la web.",
+            add_base_tools=True
+        )
+
+        # Agente de conversión de audio a texto
+        self.audio_agent = CodeAgent(
+            model=model,
+            tools=[speech_to_text_tool, final_answer],
+            max_steps=4,
+            name="audio_agent",
+            description="Este agente convierte audio a texto.",
+            add_base_tools=True
+        )
+
+        # Agente para ejecución de código Python
+        self.py_agent = CodeAgent(
+            model=model,
+            tools=[do_python, final_answer],
+            additional_authorized_imports=["json", "pandas", "numpy", "regex"],
+            max_steps=8,
+            name="python_code_agent",
+            description="Este agente ejecuta y valida código Python.",
+            add_base_tools=True
+        )
+
+        # Agente para análisis de imágenes
+        self.visual_agent = CodeAgent(
+            model=model,
+            tools=[visual_qa_tool, final_answer],
+            max_steps=4,
+            name="visual_qa_agent",
+            description="Este agente responde preguntas sobre imágenes.",
+            add_base_tools=True
+        )
+
+        # Agente principal que coordina otros agentes
+        self.manager_agent = CodeAgent(
+            model=model,
+            tools=[],
+            managed_agents=[self.web_agent, self.audio_agent, self.py_agent, self.visual_agent],
+            planning_interval=8,
+            verbosity_level=2,
+            max_steps=12,
+            add_base_tools=True
+        )
+
+    def forward(self, question: str, attachment: str = None) -> str:
+        if attachment:
+            result = self.manager_agent.run(question, additional_args={"attachment": attachment})
+        else:
+            result = self.manager_agent.run(question)
+        return result