d6

app.py

@@ -1,4 +1,3 @@
-
 import os
 import gradio as gr
 import requests
@@ -6,201 +5,205 @@ import inspect
 import pandas as pd
 from huggingface_hub import InferenceClient
 from dotenv import load_dotenv
+import json
 
 load_dotenv()
 
-# (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):
- 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 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.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
+    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 run_and_submit_all(profile: gr.OAuthProfile | None):
+    """
+    Fetches all questions, runs the BasicAgent on them, submits all answers,
+    and displays the results.
+    """
+    space_id = os.getenv("SPACE_ID")
+
+    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
+    try:
+        agent = BasicAgent()
+    except Exception as e:
+        print(f"Error instantiating agent: {e}")
+        return f"Error initializing agent: {e}", None
+
+    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 json.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 json.JSONDecodeError:
+            error_detail += f" Response: {e.response.text[:500]}"
+        status_message = f"Submission Failed: {error_detail}"
+        print(status_message)
+        return status_message, pd.DataFrame(results_log)
+    except requests.exceptions.Timeout:
+        status_message = "Submission Failed: The request timed out."
+        print(status_message)
+        return status_message, pd.DataFrame(results_log)
+    except requests.exceptions.RequestException as e:
+        status_message = f"Submission Failed: Network error - {e}"
+        print(status_message)
+        return status_message, pd.DataFrame(results_log)
+    except Exception as e:
+        status_message = f"An unexpected error occurred during submission: {e}"
+        print(status_message)
+        return status_message, pd.DataFrame(results_log)
 
 
 # --- 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]
- )
+    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, to speed up the process, you could cache answers or run asynchronously.
+        """
+    )
+
+    gr.LoginButton()
+
+    run_button = gr.Button("Run Evaluation & Submit All Answers")
+
+    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,
+        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("\n" + "-" * 30 + " App Starting " + "-" * 30)
+    space_host_startup = os.getenv("SPACE_HOST")
+    space_id_startup = os.getenv("SPACE_ID")
+
+    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(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)