Update agent.py

agent.py

@@ -0,0 +1,69 @@
+import os
+import json
+from smolagents import create_agent_executor, Agent, Task
+from langgraph.prebuilt import ToolExecutor
+from huggingface_hub import InferenceClient
+from tools import *
+
+# Load Hugging Face endpoint from environment
+HF_API_URL = os.getenv("HF_ENDPOINT_URL")  
+HF_API_TOKEN = os.getenv("HF_TOKEN") 
+
+if not HF_API_URL or not HF_API_TOKEN:
+    raise ValueError("Missing Hugging Face endpoint URL or token.")
+
+llm = InferenceClient(
+    model=HF_API_URL,
+    token=HF_API_TOKEN
+)
+
+def run_llm(prompt: str) -> str:
+    response = llm.text_generation(
+        prompt,
+        max_new_tokens=512,
+        do_sample=False,
+        temperature=0.0,
+        return_full_text=False,
+    )
+    return response.strip()
+
+tool_list = [
+    GetAttachmentTool(),
+    GoogleSearchTool(),
+    GoogleSiteSearchTool(),
+    ContentRetrieverTool(),
+    SpeechRecognitionTool(),
+    YouTubeVideoTool(),
+    ClassifierTool(),
+    ImageToChessBoardFENTool()
+]
+
+# Create tool executor for LangGraph
+tool_executor = ToolExecutor(tool_list)
+
+# Create agent instance
+agent = Agent(
+    llm=run_llm,
+    tools=tool_list,
+)
+
+agent_executor = create_agent_executor(
+    agent=agent,
+    tool_executor=tool_executor,
+    stream=False,
+)
+
+def load_tasks(metadata_path="metadata.jsonl") -> list[Task]:
+    with open(metadata_path, "r") as f:
+        tasks = []
+        for line in f:
+            data = json.loads(line)
+            tasks.append(Task(
+                task_id=data["question_id"],
+                input=data["answer"]  
+            ))
+        return tasks
+
+def solve_task(task: Task) -> str:
+    result = agent_executor.invoke(task.input)
+    return result.get("output", "")