d4

app.py

@@ -1,3 +1,4 @@
+
 import os
 import gradio as gr
 import requests
@@ -16,116 +17,190 @@ 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.token = os.getenv("token")
-        self.model_id = "meta-llama/Meta-Llama-3-70B-Instruct"
-        self.client = InferenceClient(model=self.model_id, token=self.token)
-
-    def __call__(self, question: str) -> str:
-        prompt = f"Answer this question concisely and clearly. Only return the final answer.\nQuestion: {question}"
-        try:
-            response = self.client.text_generation(prompt, max_new_tokens=100)
-            return response.strip()
-        except Exception as e:
-            print(f"Error calling inference API: {e}")
-            return f"error: {e} | token used: {self.token}"
+ def __init__(self):
+ self.token = os.getenv("agent-app")
+ self.model_id = "meta-llama/Meta-Llama-3-70B-Instruct"
+ self.client = InferenceClient(model=self.model_id, token=self.token)
+
+ def __call__(self, question: str) -> str:
+ prompt = f"Answer this question concisely and clearly. Only return the final answer.\nQuestion: {question}"
+ try:
+ response = self.client.text_generation(prompt, max_new_tokens=100)
+ return response.strip()
+ except Exception as e:
+ print(f"Error calling inference API: {e}")
+ return f"error: {e} | token used: {self.token}"
 
 def run_and_submit_all( profile: gr.OAuthProfile | 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()
-    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}")
-    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
-    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
-    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})
-        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 
-    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}")
-    try:
-        response = requests.post(submit_url, json=submission_data, timeout=60)
-        response.raise_for_status()
-        result_data = response.json()
-        final_status = (
-            f"Submission Successful!\n"
-            f"User: {result_data.get('username')}\n"
-            f"Overall Score: {result_data.get('score', 'N/A')}% "
-            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.exception
+ """
+ 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()
+ 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}")
+ 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
+ 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
+ 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})
+ 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 
+ 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}")
+ try:
+ response = requests.post(submit_url, json=submission_data, timeout=60)
+ response.raise_for_status()
+ result_data = response.json()
+ final_status = (
+ f"Submission Successful!\n"
+ f"User: {result_data.get('username')}\n"
+ f"Overall Score: {result_data.get('score', 'N/A')}% "
+ 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
+ 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 ---
+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.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,
+ 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)