Update app.py

app.py

@@ -3,56 +3,43 @@ import gradio as gr
 import requests
 import inspect
 import pandas as pd
-from smolagents import CodeAgent, DuckDuckGoSearchTool, OpenAIServerModel, WikipediaSearchTool, InferenceClientModel
+import json
+
+
 # (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-"""
-class SafeWikipediaSearch(WikipediaSearchTool):
-    def __call__(self, query: str) -> str:
-        result = super().__call__(query)
-        return str(result)  # force string
-"""
-
 # --- Basic Agent Definition ---
-class BasicAgent:
-    def __init__(self):
-        print("BasicAgent initialized.")
-
-        # Load the HF-supported model using InferenceClientModel
-        self.model = InferenceClientModel(
-            model_id="meta-llama/Llama-3.1-8B-Instruct"  #meta-llama/Llama-3.1-8B-Instruct   meta-llama/Meta-Llama-3-8B-Instruct
-                                                        #meta-llama/Llama-3.1-8B-Instruct     HuggingFaceH4/zephyr-7b-beta
-        )
-
-        # Tools
-        duckduckgo = DuckDuckGoSearchTool()
-        wiki = WikipediaSearchTool()
-        
-        # Compose the agent
-        # wiki = SafeWikipediaSearch()
-        
-        self.agent = CodeAgent(
-            tools=[duckduckgo, wiki], #wiki
-            model=self.model
-        )
-
-
-    def __call__(self, question: str) -> str:
-        print(f"Agent received question: {question[:50]}…")
-        try:
-            answer = self.agent.run(question)
-            print(f"Type of agent output: {type(answer)}")
-            print(f"Agent returning answer: {str(answer)[:80]}…")
-            return str(answer)
-        except Exception as e:
-            print(f"AGENT ERROR: {e}")
-            return f"AGENT ERROR: {e}"
-
-
+# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
+# Configure logging at the entry point
 
 
+def read_jsonl_file(file_path:str) -> dict:
+    """
+    Read a JSONL file line by line and yield each parsed JSON object.
+    
+    Args:
+        file_path (str): Path to the JSONL file
+        
+    Yields:
+        dict: Parsed JSON object from each line
+    """
+    with open(file_path, "r") as f:
+        for line in f:
+            line = line.strip()  # Remove whitespace and newlines
+            if line:  # Skip empty lines
+                try:
+                    yield json.loads(line)
+                except json.JSONDecodeError as e:
+                    continue
+
+def get_computed_answers(file_path:str):
+    answers = dict()
+    for item in read_jsonl_file(file_path):
+        answers[item["task_id"]] = {"model_answer": item["model_answer"], "reasoning_trace": item["reasoning_trace"]}
+    return answers
+    
 def run_and_submit_all( profile: gr.OAuthProfile | None):
     """
     Fetches all questions, runs the BasicAgent on them, submits all answers,
@@ -60,7 +47,7 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
     """
     # --- Determine HF Space Runtime URL and Repo URL ---
     space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code
-
+    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
     if profile:
         username= f"{profile.username}"
         print(f"User logged in: {username}")
@@ -72,17 +59,7 @@ 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)
-    try:
-        agent = BasicAgent()
-    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)
-    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
-    print(agent_code)
-
-    # 2. Fetch Questions
+    # 1. Fetch Questions
     print(f"Fetching questions from: {questions_url}")
     try:
         response = requests.get(questions_url, timeout=15)
@@ -106,6 +83,7 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
     # 3. Run your Agent
     results_log = []
     answers_payload = []
+    agent_answers = get_computed_answers("gaia_evaluation_responses.jsonl")
     print(f"Running agent on {len(questions_data)} questions...")
     for item in questions_data:
         task_id = item.get("task_id")
@@ -114,7 +92,7 @@ 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_answers[task_id]["model_answer"]
             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:
@@ -187,7 +165,6 @@ with gr.Blocks() as demo:
         **Disclaimers:**
         Once clicking on the "submit button, it can take quite some time ( this is the time for the agent to go through all the questions).
         This space provides a basic setup and is intentionally sub-optimal to encourage you to develop your own, more robust solution. For instance for the delay process of the submit button, a solution could be to cache the answers and submit in a seperate action or even to answer the questions in async.
-        Please note that this version requires an OpenAI Key to run.
         """
     )