Update app.py

app.py

@@ -3,56 +3,47 @@ import gradio as gr
 import requests
 import inspect
 import pandas as pd
-import wikipedia
+from smolagent.agents import SimpleAgent
 from transformers import pipeline
-# (Keep Constants as is)
+
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-# --- Basic Agent Definition ---
-# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
-class BasicAgent:
-    def __init__(self):
-        print("BasicAgent initialized.")
-    def __call__(self, question: str) -> str:
-        return "This is a default answer."
+# --- FLAN-T5 Agent Definition ---
+# Load the FLAN-T5 base model for question-answering
+qa_pipeline = pipeline("text2text-generation", model="google/flan-t5-base")
 
-class EchoAgent:
+# Define a tool that wraps the FLAN-T5 model
+class FlanT5Tool:
     def __init__(self):
-        print("EchoAgent initialized.")
-    def __call__(self, question: str) -> str:
-        return f"You asked: {question}"
+        self.name = "flan_t5_qa"
+        self.description = "Answers questions using FLAN-T5 base."
 
-class WikipediaAgent:
-    def __init__(self):
-        wikipedia.set_lang("en")
     def __call__(self, question: str) -> str:
-        try:
-            # Extract potential keyword
-            query = question.split(" ")[-1]  # naive keyword pick
-            page = wikipedia.page(query)
-            return wikipedia.summary(page.title, sentences=1)
-        except Exception as e:
-            return "Could not answer"
-class TransformersAgent:
+        print(f"FlanT5Tool received: {question}")
+        response = qa_pipeline(question, max_new_tokens=100)
+        return response[0]['generated_text']
+
+# Instantiate the tool
+flan_tool = FlanT5Tool()
+
+# Create a simple agent with the FLAN-T5 tool
+class SmolAgentWrapper:
     def __init__(self):
-        print("Loading transformers pipeline (flan-t5-base)...")
-        self.model = pipeline("text2text-generation", model="google/flan-t5-base", device=-1)  # CPU only
+        self.agent = SimpleAgent(tools=[flan_tool])
 
     def __call__(self, question: str) -> str:
-        try:
-            response = self.model(question, max_new_tokens=100)
-            return response[0]['generated_text'].strip()
-        except Exception as e:
-            return f"Error: {str(e)}"
-            
-def run_and_submit_all(agent_name: str, profile: gr.OAuthProfile | None = None):
+        print(f"SmolAgentWrapper received: {question}")
+        return self.agent.run(question)
+
+# --- Run and Submit Logic ---
+def run_and_submit_all(profile: gr.OAuthProfile | None):
     """
-    Fetches all questions, runs the BasicAgent on them, submits all answers,
+    Fetches all questions, runs the agent on them, submits all answers,
     and displays the results.
     """
     # --- Determine HF Space Runtime URL and Repo URL ---
-    space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code
+    space_id = os.getenv("SPACE_ID")
 
     if profile:
         username= f"{profile.username}"
@@ -65,22 +56,12 @@ def run_and_submit_all(agent_name: str, profile: gr.OAuthProfile | None = 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 (with FLAN-T5)
     try:
-        if agent_name == "BasicAgent":
-            agent = BasicAgent()
-        elif agent_name == "EchoAgent":
-            agent = EchoAgent()
-        elif agent_name == "WikipediaAgent":
-            agent = WikipediaAgent()
-        elif agent_name == "TransformersAgent":
-            agent = TransformersAgent()
-        else:
-            return f"Unknown agent selected: {agent_name}", None
+        agent = SmolAgentWrapper()
     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)
 
@@ -91,8 +72,8 @@ def run_and_submit_all(agent_name: str, profile: gr.OAuthProfile | None = None):
         response.raise_for_status()
         questions_data = response.json()
         if not questions_data:
-             print("Fetched questions list is empty.")
-             return "Fetched questions list is empty or invalid format.", None
+            print("Fetched questions list is empty.")
+            return "Fetched questions list is empty or invalid format.", None
         print(f"Fetched {len(questions_data)} questions.")
     except requests.exceptions.RequestException as e:
         print(f"Error fetching questions: {e}")
@@ -127,7 +108,7 @@ def run_and_submit_all(agent_name: str, profile: gr.OAuthProfile | None = 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)
@@ -178,46 +159,30 @@ def run_and_submit_all(agent_name: str, profile: gr.OAuthProfile | None = None):
 
 # --- Build Gradio Interface using Blocks ---
 with gr.Blocks() as demo:
-    gr.Markdown("# GAIA Agent Evaluator")
+    gr.Markdown("# Smol Agent Evaluation Runner")
+    gr.Markdown(
+        """
+        **Instructions:**
+        1.  Log in to your Hugging Face account using the button below.
+        2.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
+        ---
+        **Disclaimers:**
+        This space uses a small agent powered by FLAN-T5 base for answering questions.
+        """
+    )
 
     gr.LoginButton()
-    
-    agent_dropdown = gr.Dropdown(
-        choices=["BasicAgent", "EchoAgent", "WikipediaAgent", "TransformersAgent"],
-        label="Select Agent",
-        value="BasicAgent"
-    )
 
     run_button = gr.Button("Run Evaluation & Submit All Answers")
-    status_output = gr.Textbox(label="Status / Score", lines=5, interactive=False)
-    results_table = gr.DataFrame(label="Agent Answers", wrap=True)
+
+    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
+    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
 
     run_button.click(
         fn=run_and_submit_all,
-        inputs=[agent_dropdown],
         outputs=[status_output, results_table]
     )
 
 if __name__ == "__main__":
-    print("\n" + "-"*30 + " App Starting " + "-"*30)
-    # Check for SPACE_HOST and SPACE_ID at startup for information
-    space_host_startup = os.getenv("SPACE_HOST")
-    space_id_startup = os.getenv("SPACE_ID") # Get SPACE_ID at startup
-
-    if space_host_startup:
-        print(f"✅ SPACE_HOST found: {space_host_startup}")
-        print(f"   Runtime URL should be: https://{space_host_startup}.hf.space")
-    else:
-        print("ℹ️  SPACE_HOST environment variable not found (running locally?).")
-
-    if space_id_startup: # Print repo URLs if SPACE_ID is found
-        print(f"✅ SPACE_ID found: {space_id_startup}")
-        print(f"   Repo URL: https://huggingface.co/spaces/{space_id_startup}")
-        print(f"   Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
-    else:
-        print("ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")
-
-    print("-"*(60 + len(" App Starting ")) + "\n")
-
-    print("Launching Gradio Interface for Basic Agent Evaluation...")
-    demo.launch(debug=True, share=False)
+    print("Launching Gradio Interface for Smol Agent Evaluation...")
+    demo.launch(debug=True, share=False)