Update multiagents.py

multiagents.py

@@ -1,8 +1,10 @@
-# multiagent.py — GAIA-compliant smolagents setup using Groq
-
+# multiagent.py — GAIA-compliant multi-agent system using Groq (patched)
 import os
 import dotenv
-from smolagents import CodeAgent, OpenAIServerModel, PythonInterpreterTool
+
+from smolagents import CodeAgent, PythonInterpreterTool
+from smolagents.models.openai_server_model import OpenAIServerModel as BaseOpenAIServerModel
+
 from tools.fetch import fetch_webpage, search_web
 from tools.yttranscript import get_youtube_transcript, get_youtube_title_description
 from tools.stt import get_text_transcript_from_audio_file
@@ -10,32 +12,40 @@ from tools.image import analyze_image
 from common.mylogger import mylog
 import myprompts
 
+# ✅ Load .env
 dotenv.load_dotenv()
 
-# ✅ Use Groq’s LLaMA3 (OpenAI-compatible and fast)
-groq_model = OpenAIServerModel(
+# ✅ Monkeypatch: Ensure message['content'] is always a string
+class PatchedOpenAIServerModel(BaseOpenAIServerModel):
+    def complete_chat(self, messages, **kwargs):
+        for msg in messages:
+            if not isinstance(msg.get("content", ""), str):
+                msg["content"] = str(msg["content"])
+        return super().complete_chat(messages, **kwargs)
+
+# ✅ Groq model (OpenAI-compatible)
+groq_model = PatchedOpenAIServerModel(
     model_id="llama3-70b-8192",
     api_key=os.environ["GROQ_API_KEY"],
     api_base="https://api.groq.com/openai/v1",
 )
 
-# ✅ Final answer validation
+# ✅ Final answer checker
 def check_final_answer(final_answer, agent_memory) -> bool:
     mylog("check_final_answer", final_answer)
     return len(str(final_answer)) <= 200
 
-# ✅ Web agent for search and scraping
+# ✅ Sub-agents
 web_agent = CodeAgent(
     model=groq_model,
     tools=[search_web, fetch_webpage],
     name="web_agent",
-    description="Use search engine to find webpages related to a subject and get the page content.",
+    description="Uses search engine and scrapes webpages for content.",
     additional_authorized_imports=["pandas", "numpy", "bs4"],
     verbosity_level=1,
     max_steps=7,
 )
 
-# ✅ Audio/Video/Image processing agent
 audiovideo_agent = CodeAgent(
     model=groq_model,
     tools=[
@@ -45,30 +55,30 @@ audiovideo_agent = CodeAgent(
         analyze_image
     ],
     name="audiovideo_agent",
-    description="Extracts information from image, video or audio files from the web.",
+    description="Extracts data from audio, video, or images.",
     additional_authorized_imports=["pandas", "numpy", "bs4", "requests"],
     verbosity_level=1,
     max_steps=7,
 )
 
-# ✅ Manager agent (planner/coordinator)
+# ✅ Manager agent
 manager_agent = CodeAgent(
     model=groq_model,
     tools=[PythonInterpreterTool()],
     managed_agents=[web_agent, audiovideo_agent],
+    name="manager_agent",
+    description="Coordinates other agents and returns a final answer.",
     additional_authorized_imports=["pandas", "numpy", "bs4"],
     planning_interval=5,
     verbosity_level=2,
     final_answer_checks=[check_final_answer],
     max_steps=15,
-    name="manager_agent",
-    description="A manager agent that coordinates the work of other agents to answer questions.",
 )
 
-# ✅ Multi-agent system wrapper class
+# ✅ Multi-agent interface
 class MultiAgent:
     def __init__(self):
-        print("MultiAgent system initialized.")
+        print("MultiAgent initialized.")
 
     def __call__(self, question: str) -> str:
         mylog(self.__class__.__name__, question)
@@ -80,15 +90,15 @@ You can use the web_agent to search the web, or the audiovideo_agent to extract
 You must reason step by step and respect the required output format.
 Only return the final answer in the correct format.
 """
-            full_prompt = prefix.strip() + "\nTHE QUESTION:\n" + question.strip() + "\n" + myprompts.output_format.strip()
-            answer = manager_agent.run(full_prompt)
+            prompt = prefix.strip() + "\nTHE QUESTION:\n" + question.strip() + "\n" + myprompts.output_format.strip()
+            answer = manager_agent.run(prompt)
             return answer
         except Exception as e:
             error = f"An error occurred while processing the question: {e}"
             print(error)
             return error
 
-# ✅ Manual test (can be triggered from HF Space too)
+# ✅ Local test
 if __name__ == "__main__":
     question = "What was the actual enrollment of the Malko competition in 2023?"
     agent = MultiAgent()