add llama-index agent (without tools for now)

app.py

@@ -1,9 +1,13 @@
 import os
 import gradio as gr
 import requests
-import inspect
 import pandas as pd
 
+from llama_index.llms.huggingface_api import HuggingFaceInferenceAPI
+from llama_index.core.agent.workflow import ReActAgent
+from llama_index.core.workflow import Context
+from llama_index.core.agent.workflow import AgentStream, ToolCallResult
+
 # (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
@@ -19,7 +23,29 @@ class BasicAgent:
         print(f"Agent returning fixed answer: {fixed_answer}")
         return fixed_answer
 
-def run_and_submit_all( profile: gr.OAuthProfile | None):
+class LLamaIndexAgent:
+    def __init__(self, model_name="Qwen/Qwen2.5-Coder-32B-Instruct"):
+        llm = HuggingFaceInferenceAPI(model_name=model_name)
+        self.agent = ReActAgent(tools=[], llm=llm)
+        self.ctx = Context(self.agent)
+        print(f"BasicAgent initialized with model \"{model_name}\"")
+
+    async def __call__(self, question: str) -> str:
+        print(f"Agent received question (first 50 chars): {question[:50]}...")
+
+        handler = self.agent.run(question, ctx=self.ctx)
+        async for ev in handler.stream_events():
+            if isinstance(ev, ToolCallResult):
+                print(f"\nCall {ev.tool_name} with {ev.tool_kwargs}\nReturned: {ev.tool_output}")
+            if isinstance(ev, AgentStream):
+                print(f"{ev.delta}", end="", flush=True)
+
+        response = await handler
+        print("\n\n"+"-"*50)
+        print(f"Agent returning with answer: {response}")
+        return response
+
+async def run_and_submit_all(profile: gr.OAuthProfile | None):
     """
     Fetches all questions, runs the BasicAgent on them, submits all answers,
     and displays the results.
@@ -38,13 +64,14 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
     questions_url = f"{api_url}/questions"
     submit_url = f"{api_url}/submit"
 
-    # 1. Instantiate Agent ( modify this part to create your agent)
+    # 1. Instantiate Agent (modify this part to create your agent)
     try:
-        agent = BasicAgent()
+        # agent = BasicAgent()
+        agent = LLamaIndexAgent()
     except Exception as e:
         print(f"Error instantiating agent: {e}")
         return f"Error initializing agent: {e}", None
-    # In the case of an app running as a hugging Face space, this link points toward your codebase ( usefull for others so please keep it public)
+    # In the case of an app running as a hugging Face space, this link points toward your codebase (useful for others so please keep it public)
     agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
     print(agent_code)
 
@@ -62,9 +89,9 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
         print(f"Error fetching questions: {e}")
         return f"Error fetching questions: {e}", None
     except requests.exceptions.JSONDecodeError as e:
-         print(f"Error decoding JSON response from questions endpoint: {e}")
-         print(f"Response text: {response.text[:500]}")
-         return f"Error decoding server response for questions: {e}", None
+        print(f"Error decoding JSON response from questions endpoint: {e}")
+        print(f"Response text: {response.text[:500]}")
+        return f"Error decoding server response for questions: {e}", None
     except Exception as e:
         print(f"An unexpected error occurred fetching questions: {e}")
         return f"An unexpected error occurred fetching questions: {e}", None
@@ -80,7 +107,8 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
             print(f"Skipping item with missing task_id or question: {item}")
             continue
         try:
-            submitted_answer = agent(question_text)
+            # submitted_answer = agent(question_text)
+            submitted_answer = await agent(question_text)
             answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
             results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
         except Exception as e:
@@ -91,7 +119,7 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
         print("Agent did not produce any answers to submit.")
         return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
 
-    # 4. Prepare Submission 
+    # 4. Prepare Submission
     submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
     status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
     print(status_update)

requirements.txt

@@ -1,2 +1,4 @@
 gradio
-requests
+requests
+llama-index
+llama-index-llms-huggingface-api