agent basic initialization for qa

agent.py

@@ -1,15 +1,24 @@
-from smolagents import CodeAgent, InferenceClientModel, ToolCallingAgent, DuckDuckGoSearchTool
+from smolagents import (
+    CodeAgent,
+    InferenceClientModel,
+    ToolCallingAgent,
+    DuckDuckGoSearchTool,
+    HfApiModel,
+    LiteLLMModel,
+    tool
+)
 from dotenv import load_dotenv
+from typing import Optional, List, Dict, Any
 import os
 load_dotenv()
 model_id = "meta-llama/Llama-3.3-70B-Instruct" 
 
-
+@tool
 def basic_inference(
     prompt: str,
     model_id: str = "meta-llama/Llama-3.3-70B-Instruct",
     provider: str = "groq",
-):
+) -> str:
     """
     Run a basic inference using the specified model and provider.
 
@@ -31,11 +40,12 @@ def basic_inference(
     # Run the agent with the provided prompt
     return agent.run(prompt)
 
+@tool
 def toolcalling(
     prompt: str,
     model_id: str = "meta-llama/Llama-3.3-70B-Instruct",
     provider: str = "groq",
-):
+) -> str:
     """
     Run a tool calling inference using the specified model and provider.
 
@@ -57,6 +67,7 @@ def toolcalling(
     # Run the agent with the provided prompt
     return agent.run(prompt)
 
+@tool
 def web_search(query: str) -> str:
     """Search DuckDuckGo for a query and return maximum 3 result.
 
@@ -64,4 +75,67 @@ def web_search(query: str) -> str:
         query: The search query."""
     search_tool = DuckDuckGoSearchTool()
     search_docs = search_tool(query)
-    return search_docs
+    return search_docs
+
+
+class BotMan:
+    def __init__(self,
+        model_type: str = "HfApiModel",
+        model_id: Optional[str] = None,
+        api_key: Optional[str] = None,
+        provider: Optional[str] = None,
+        timeout: Optional[int] = None,
+        temperature: Optional[float] = 0,
+        additional_imports: List[str] = None,
+        executor_type: str = "local",
+                 ):
+        """
+        Initialize the BotMan class.
+        """
+        if model_type == "HfApiModel":
+            if api_key is None:
+                api_key = os.environ.get("HUGGINGFACEHUB_API_KEY")
+                if not api_key:
+                    raise ValueError("API key is required for HfApiModel.")
+        self.model = InferenceClientModel(
+            model_id=model_id or "meta-llama/Llama-3.3-70B-Instruct",
+            token=api_key,
+            provider=provider or "hf-inference",
+            temperature=temperature,
+            timeout=timeout or 80
+        )
+        self.tools = [
+            web_search,
+            basic_inference,
+            toolcalling,
+        ]
+        executor_kwargs = {}
+        self.imports = ["pandas", "numpy", "datetime", "json", "re", "math", "os", "requests", "csv", "urllib"]
+        if additional_imports:
+            self.imports.extend(additional_imports)
+
+        self.agent = CodeAgent(
+            tools=self.tools,
+            model=self.model,
+            additional_authorized_imports=self.imports,
+            executor_type=executor_type,
+            executor_kwargs=executor_kwargs,
+        )
+
+    def answer(self, question: str) -> str:
+        """
+        Answer a question using the agent.
+        """
+        try:
+            result = self.agent.run(question)
+            return result
+        except Exception as e:
+            print(f"Error during inference: {e}")
+            return str(e)
+
+if __name__ == '__main__':
+    # Example usage
+    bot = BotMan(model_type="HfApiModel", model_id=model_id, api_key=os.environ.get("HUGGINGFACEHUB_API_TOKEN"))
+    question = "What is the capital of France?"
+    answer = bot.answer(question)
+    print(answer)

api_integration.py

@@ -0,0 +1,48 @@
+import requests
+from typing import List, Dict, Any
+# from core_agent import GAIAAgent
+
+
+class GAIAApiClient:
+    def __init__(self, api_url="https://agents-course-unit4-scoring.hf.space"):
+        self.api_url = api_url
+        self.questions_url = f"{api_url}/questions"
+        self.submit_url = f"{api_url}/submit"
+        self.files_url = f"{api_url}/files"
+
+    def get_questions(self) -> List[Dict[str, Any]]:
+        """Fetch all evaluation questions"""
+        response = requests.get(self.questions_url)
+        response.raise_for_status()
+        return response.json()
+
+    def get_random_question(self) -> Dict[str, Any]:
+        """Fetch a single random question"""
+        response = requests.get(f"{self.api_url}/random-question")
+        response.raise_for_status()
+        return response.json()
+
+    def get_file(self, task_id: str) -> bytes:
+        """Download a file for a specific task"""
+        response = requests.get(f"{self.files_url}/{task_id}")
+        response.raise_for_status()
+        return response.content
+
+    def submit_answers(self, username: str, agent_code: str, answers: List[Dict[str, Any]]) -> Dict[str, Any]:
+        """Submit agent answers and get score"""
+        data = {
+            "username": username,
+            "agent_code": agent_code,
+            "answers": answers
+        }
+        response = requests.post(self.submit_url, json=data)
+        response.raise_for_status()
+        return response.json()
+
+if __name__ == '__main__':
+    # Example usage
+    api_client = GAIAApiClient()
+    questions = api_client.get_questions()
+    for q in questions:
+        print(q)
+

app.py

@@ -3,7 +3,8 @@ import gradio as gr
 import requests
 import inspect
 import pandas as pd
-
+from dotenv import load_dotenv
+load_dotenv()
 # (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"