+ merged with gradio for benchmarking

agents.py

@@ -35,7 +35,7 @@ from tools import (
 
 load_dotenv("env.local")
 
-class AlfredAgent:
+class LlamaIndexAgent:
     def __init__(self):
         # Tool initializations using imported functions
         self.tavily_tool = get_tavily_tool()
@@ -46,11 +46,17 @@ class AlfredAgent:
         self.search_tool = get_search_tool()
         self.calculator_tool = get_calculator_tool()
         self.hub_stats_tool = get_hub_stats_tool()
+        with open("system_prompt.txt", "r") as f:
+            self.system_prompt = f.read()
+
+        print(self.system_prompt)
         # LLM and agent workflow
         self.llm = HuggingFaceInferenceAPI(model_name="Qwen/Qwen2.5-Coder-32B-Instruct")
         self.alfred = AgentWorkflow.from_tools_or_functions(
             [*self.search_tool, *self.calculator_tool, self.wikipedia_tool, self.arxiv_tool, self.hub_stats_tool],
-            llm=self.llm
+            llm=self.llm,
+            system_prompt=self.system_prompt
+            # verbose=True
         )
 
         LANGFUSE_AUTH=base64.b64encode(f"{os.getenv('LANGFUSE_PUBLIC_KEY')}:{os.getenv('LANGFUSE_SECRET_KEY')}".encode()).decode()
@@ -93,12 +99,12 @@ class AlfredAgent:
 
 def main():
     
-    agent = AlfredAgent()
-    query = "who is the capital of France?"
+    agent = LlamaIndexAgent()
+    query = "what is the capital of maharashtra?"
     print(f"Running query: {query}")
     response = asyncio.run(agent.run_query(query))
     print("\n🎩 Agents's Response:")
-    pprint(response)
+    print(response)
 
 if __name__ == "__main__":
     main()

app.py

@@ -3,7 +3,7 @@ import gradio as gr
 import requests
 import inspect
 import pandas as pd
-
+from agents import LlamaIndexAgent
 # (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
@@ -12,12 +12,13 @@ DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
 class BasicAgent:
     def __init__(self):
+        self.agent = LlamaIndexAgent()
         print("BasicAgent initialized.")
     def __call__(self, question: str) -> str:
         print(f"Agent received question (first 50 chars): {question[:50]}...")
-        fixed_answer = "This is a default answer."
-        print(f"Agent returning fixed answer: {fixed_answer}")
-        return fixed_answer
+        response = self.agent.run_query(question)
+        print(f"Agent returning fixed answer: {response}")
+        return response
 
 def run_and_submit_all( profile: gr.OAuthProfile | None):
     """

system_prompt.txt

@@ -0,0 +1 @@
+give single word answer and reply.