Gary Simmons
add YouTube video analysis tools and audio transcription capabilities, including documentation and test scripts
5e5f9d1
| import os | |
| import gradio as gr | |
| import requests | |
| import pandas as pd | |
| import time | |
| import threading | |
| import random | |
| from litellm import RateLimitError | |
| import os | |
| from smolagents import ( | |
| CodeAgent, | |
| DuckDuckGoSearchTool, | |
| VisitWebpageTool, | |
| PythonInterpreterTool, | |
| WikipediaSearchTool, | |
| SpeechToTextTool, | |
| LiteLLMModel, | |
| tool, | |
| ) | |
| from tools import analyze_youtube_video, get_youtube_video_info, transcribe_audio | |
| # (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 TokenBucketRateLimiter: | |
| """Simple token-bucket rate limiter. | |
| capacity: max tokens in bucket (burst size) | |
| refill_rate: tokens added per second | |
| """ | |
| def __init__(self, capacity: int, refill_rate: float): | |
| self.capacity = float(capacity) | |
| self._tokens = float(capacity) | |
| self.refill_rate = float(refill_rate) | |
| self._lock = threading.Lock() | |
| self._last = time.monotonic() | |
| def acquire(self, tokens: float = 1.0): | |
| with self._lock: | |
| now = time.monotonic() | |
| elapsed = now - self._last | |
| # Refill | |
| self._tokens = min(self.capacity, self._tokens + elapsed * self.refill_rate) | |
| self._last = now | |
| if self._tokens >= tokens: | |
| self._tokens -= tokens | |
| return 0.0 | |
| # Need to wait for enough tokens | |
| required = tokens - self._tokens | |
| wait_time = required / self.refill_rate | |
| # consume what will be available after waiting | |
| self._tokens = 0.0 | |
| return wait_time | |
| class RateLimitedModel: | |
| """Wraps a model-like callable and enforces a TokenBucketRateLimiter before each call with retry logic.""" | |
| def __init__( | |
| self, | |
| model_obj, | |
| rpm: int = 8, | |
| burst: int | None = None, | |
| max_retries: int = 10, | |
| base_delay: float = 30.0, | |
| ): | |
| self._model = model_obj | |
| self.max_retries = max_retries | |
| self.base_delay = base_delay | |
| # rpm -> tokens per minute | |
| capacity = burst if burst is not None else max(1, rpm) | |
| refill_rate = float(rpm) / 60.0 | |
| self._limiter = TokenBucketRateLimiter( | |
| capacity=capacity, refill_rate=refill_rate | |
| ) | |
| def _call_with_retry(self, func, *args, **kwargs): | |
| """Call a function with retry logic for rate limit errors.""" | |
| last_exception = None | |
| for attempt in range(1, self.max_retries + 1): | |
| try: | |
| # Apply rate limiting before each attempt | |
| wait = self._limiter.acquire(1.0) | |
| if wait > 0: | |
| jitter = random.uniform(0.0, 0.5) | |
| total_wait = wait + jitter | |
| print( | |
| f"RateLimitedModel sleeping {total_wait:.2f}s to respect RPM limit" | |
| ) | |
| time.sleep(total_wait) | |
| print(f"Model call attempt {attempt} of {self.max_retries}") | |
| result = func(*args, **kwargs) | |
| print(f"Model call attempt {attempt} succeeded") | |
| return result | |
| except Exception as e: | |
| last_exception = e | |
| error_str = str(e).lower() | |
| # Check if this is a rate limit error (various ways it might be reported) | |
| is_rate_limit = ( | |
| isinstance(e, RateLimitError) | |
| or "rate limit" in error_str | |
| or "quota" in error_str | |
| or "429" in error_str | |
| or "resource_exhausted" in error_str | |
| or "too many requests" in error_str | |
| ) | |
| if is_rate_limit: | |
| print(f"Rate limit error on attempt {attempt}: {e}") | |
| if attempt < self.max_retries: | |
| # Use a longer delay for rate limit errors | |
| delay = self.base_delay + random.uniform(0, 5) | |
| print(f"Waiting {delay:.1f}s before retry {attempt + 1}...") | |
| time.sleep(delay) | |
| continue | |
| else: | |
| # Non-rate-limit error, don't retry | |
| print(f"Non-rate-limit error on attempt {attempt}: {e}") | |
| raise e | |
| # All retries exhausted | |
| print(f"All {self.max_retries} attempts failed. Raising last exception.") | |
| raise last_exception | |
| def __call__(self, *args, **kwargs): | |
| return self._call_with_retry(self._model, *args, **kwargs) | |
| def __getattr__(self, name: str): | |
| """Proxy attribute access to the underlying model. | |
| For callable attributes (like `generate`) we wrap the call so the | |
| token-bucket rate limiter and retry logic are applied consistently. | |
| """ | |
| # Avoid recursion | |
| if name.startswith("_"): | |
| raise AttributeError(name) | |
| attr = getattr(self._model, name) | |
| if callable(attr): | |
| def wrapped(*args, **kwargs): | |
| return self._call_with_retry(attr, *args, **kwargs) | |
| # Preserve original metadata where possible | |
| try: | |
| wrapped.__name__ = getattr(attr, "__name__", wrapped.__name__) | |
| except Exception: | |
| pass | |
| return wrapped | |
| return attr | |
| # Wrap the model with a rate-limiter and retry logic. Default RPM is reduced to 8 | |
| # but can be configured via the MODEL_RPM environment variable. | |
| _configured_rpm = int(os.getenv("MODEL_RPM", "8")) | |
| _configured_burst = None | |
| _configured_max_retries = int(os.getenv("MODEL_MAX_RETRIES", "10")) | |
| _configured_base_delay = float(os.getenv("MODEL_BASE_DELAY", "30.0")) | |
| model = RateLimitedModel( | |
| LiteLLMModel(model_id="gemini/gemini-2.5-flash", temperature=0.2), | |
| rpm=_configured_rpm, | |
| burst=_configured_burst, | |
| max_retries=_configured_max_retries, | |
| base_delay=_configured_base_delay, | |
| ) | |
| class BasicAgent: | |
| def __init__(self, name: str = "GGSAgent"): | |
| self.name = name | |
| self.code_agent = CodeAgent( | |
| tools=[ | |
| DuckDuckGoSearchTool(), | |
| VisitWebpageTool(), | |
| PythonInterpreterTool(), | |
| WikipediaSearchTool(), | |
| SpeechToTextTool(), | |
| transcribe_audio, | |
| analyze_youtube_video, | |
| get_youtube_video_info, | |
| ], | |
| model=model, | |
| max_steps=20, | |
| verbosity_level=1, | |
| additional_authorized_imports=[ | |
| "json", | |
| "math", | |
| "pandas", | |
| "yt_dlp", | |
| "tempfile", | |
| "os", | |
| "torch", | |
| "whisper", | |
| "re", | |
| "litellm", | |
| "requests", | |
| "time", | |
| "threading", | |
| "random", | |
| "cv2", | |
| "numpy", | |
| "PIL", | |
| "base64", | |
| "io", | |
| "pathlib", | |
| "subprocess", | |
| ], | |
| add_base_tools=True, | |
| ) | |
| print("BasicAgent initialized.") | |
| def __call__(self, question: str) -> str: | |
| print(f"Agent received question (first 50 chars): {question[:50]}...") | |
| print(f"Starting agent execution with model retry logic enabled...") | |
| start_time = time.time() | |
| try: | |
| # The retry logic is now handled at the model level within RateLimitedModel | |
| # so we can call the agent directly | |
| response = self.code_agent(question) | |
| duration = time.time() - start_time | |
| print(f"Agent completed successfully in {duration:.1f}s") | |
| print(f"Agent returning response: {response}") | |
| return response | |
| except Exception as e: | |
| duration = time.time() - start_time | |
| print(f"Error in code agent after {duration:.1f}s: {e}") | |
| return f"AGENT ERROR: {e}" | |
| 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_name = os.getenv("AGENT_NAME", "GGSAgent") | |
| agent = BasicAgent(name=agent_name) | |
| 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, | |
| "agent_name": getattr(agent, "name", "BasicAgent"), | |
| "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) |