Update app.py

app.py

@@ -1,147 +1,88 @@
 import os
 import gradio as gr
 import requests
-import inspect
 import pandas as pd
-import re
 
-from smolagents import CodeAgent, DuckDuckGoSearchTool,InferenceClientModel , PythonInterpreterTool, WikipediaSearchTool, FinalAnswerTool
- 
-# (Keep Constants as is)
+from smolagents import CodeAgent, DuckDuckGoSearchTool, InferenceClientModel, PythonInterpreterTool, WikipediaSearchTool, FinalAnswerTool
+
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
- 
-# --- Basic Agent Definition ---
-# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
 
-import os
-from smolagents import InferenceClientModel
+# --- Hugging Face API Key ---
+HF_API_KEY = "your_huggingface_api_key_here"  # <-- Replace with your key
 
+# --- Initialize Hugging Face Model ---
 model = InferenceClientModel(
     model_id="Qwen/Qwen1.5-1.8B-Chat",  # example HF chat model
-    token=os.getenv("HF_TOKEN")          # only needed if private model
+    token=HF_API_KEY
 )
 
-try:
-    import ddgs
-except ImportError:
-    from duckduckgo_search import DDGS
-    import sys, types
-    ddgs = types.SimpleNamespace(DDGS=DDGS)
-    sys.modules["ddgs"] = ddgs
-
+# --- Basic Agent Definition ---
 class BasicAgent:
-    def __init__(self, api_key: str = None):
-        if not api_key:
-            print("Did not receive API key.")
-        else:
-            print(f"First 5 characters of the provided API key: {api_key[:5]}")
-            os.environ["HF_TOKEN"] = api_key
-        print("BasicAgent initialized.")
+    def __init__(self, model):
+        self.model = model
+        self.agent = CodeAgent(
+            tools=[
+                DuckDuckGoSearchTool(),
+                PythonInterpreterTool(),
+                WikipediaSearchTool(),
+                FinalAnswerTool()
+            ],
+            model=self.model
+        )
+        print("BasicAgent initialized with Hugging Face model.")
+
     def __call__(self, question: str) -> str:
         print(f"Agent received question (first 50 chars): {question[:50]}...")
-        agent = CodeAgent(tools=[DuckDuckGoSearchTool(),PythonInterpreterTool(),WikipediaSearchTool(),FinalAnswerTool()],model=OpenAIServerModel(model_id="gpt-4o"))
-        answer=agent.run(question)
+        try:
+            answer = self.agent.run(question)
+        except Exception as e:
+            answer = f"AGENT ERROR: {e}"
         return answer
- 
-def run_and_submit_all(api_key: str, profile: gr.OAuthProfile | None = None):
-    """
-    Fetches all questions, runs the BasicAgent 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
- 
-    if profile:
-        username= f"{profile.username}"
-        print(f"User logged in: {username}")
-    else:
-        print("User not logged in.")
-        return "Please Login to Hugging Face with the button.", None
- 
-    api_url = DEFAULT_API_URL
-    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(model=model)
-    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
-    print(f"Fetching questions from: {questions_url}")
+
+# --- Run & Submit Function ---
+def run_and_submit_all(profile: gr.OAuthProfile | None = None):
+    if not profile:
+        return "Please login to Hugging Face with the button.", None
+    username = profile.username
+    print(f"User logged in: {username}")
+
+    # Initialize agent
+    agent = BasicAgent(model=model)
+
+    # Fetch questions
+    questions_url = f"{DEFAULT_API_URL}/questions"
+    submit_url = f"{DEFAULT_API_URL}/submit"
     try:
         response = requests.get(questions_url, timeout=15)
         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(f"Fetched {len(questions_data)} questions.")
-    except requests.exceptions.RequestException as e:
-        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
+            return "Fetched questions list is empty or invalid format.", None
     except Exception as e:
-        print(f"An unexpected error occurred fetching questions: {e}")
-        return f"An unexpected error occurred fetching questions: {e}", None
- 
-    # 3. Run your Agent
+        return f"Error fetching questions: {e}", None
+
+    # Run agent on all questions
     results_log = []
     answers_payload = []
-    print(f"Running agent on {len(questions_data)} questions...")
     for item in questions_data:
         task_id = item.get("task_id")
         question_text = item.get("question")
         if not task_id or question_text is None:
-            print(f"Skipping item with missing task_id or question: {item}")
             continue
-        try:
-            submitted_answer = 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})
-
-
-
-
-
-
-
-
-
-
-
+        submitted_answer = 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:
-             print(f"Error running agent on task {task_id}: {e}")
-             results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})
- 
     if not answers_payload:
-        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 
+
+    # Prepare submission
+    space_id = os.getenv("SPACE_ID", "your_space_id_here")  # optional fallback
+    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
     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)
- 
-    # 5. Submit
-    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
+
+    # Submit
     try:
         response = requests.post(submit_url, json=submission_data, timeout=60)
         response.raise_for_status()
@@ -153,106 +94,29 @@ def run_and_submit_all(api_key: str, profile: gr.OAuthProfile | None = None):
             f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n"
             f"Message: {result_data.get('message', 'No message received.')}"
         )
-        print("Submission successful.")
-        results_df = pd.DataFrame(results_log)
-        return final_status, results_df
-    except requests.exceptions.HTTPError as e:
-        error_detail = f"Server responded with status {e.response.status_code}."
-        try:
-            error_json = e.response.json()
-            error_detail += f" Detail: {error_json.get('detail', e.response.text)}"
-        except requests.exceptions.JSONDecodeError:
-            error_detail += f" Response: {e.response.text[:500]}"
-        status_message = f"Submission Failed: {error_detail}"
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-    except requests.exceptions.Timeout:
-        status_message = "Submission Failed: The request timed out."
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-    except requests.exceptions.RequestException as e:
-        status_message = f"Submission Failed: Network error - {e}"
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
+        return final_status, pd.DataFrame(results_log)
     except Exception as e:
-        status_message = f"An unexpected error occurred during submission: {e}"
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
- 
- 
-# --- Build Gradio Interface using Blocks ---
-
-
-
-
-
-
-
-
-
-
-
-
-
+        return f"Submission Failed: {e}", pd.DataFrame(results_log)
 
+# --- Gradio Interface ---
 with gr.Blocks() as demo:
     gr.Markdown("# Basic Agent Evaluation Runner")
-    gr.Markdown(
-        """
-        **Instructions:**
- 
-        1.  Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
-        2.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.
-        3.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
- 
-        ---
-        **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.
-        """
-    )
- 
+    gr.Markdown("""
+    **Instructions:**
+    1. Login with your Hugging Face account.
+    2. Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, and submit.
+    """)
 
     gr.LoginButton()
- 
     run_button = gr.Button("Run Evaluation & Submit All Answers")
- 
     status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
-    # Removed max_rows=10 from DataFrame constructor
     results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
- 
+
     run_button.click(
         fn=run_and_submit_all,
-        input=(HF_Api_key)
+        inputs=(),
         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)
+    demo.launch(debug=True, share=False)