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| import os | |
| import gradio as gr | |
| import requests | |
| import inspect | |
| import pandas as pd | |
| from smolagents import ( | |
| CodeAgent, | |
| LiteLLMModel, | |
| DuckDuckGoSearchTool, | |
| LogLevel, | |
| load_tool, | |
| PythonInterpreterTool | |
| ) | |
| from dotenv import load_dotenv | |
| from smolagents import Tool | |
| import base64 | |
| import anthropic | |
| from PIL import Image | |
| import io | |
| class SimpleExcelTool(Tool): | |
| name = "SimpleExcelTool" | |
| description = "Load a downloaded Excel file associated with a task ID and perform basic operations like reading data" | |
| inputs = { | |
| "task_id": { | |
| "type": "string", | |
| "description": "Task ID for which the Excel file has been downloaded" | |
| }, | |
| "operation": { | |
| "type": "string", | |
| "description": "Operation to perform on the Excel file (currently only 'read' is supported)", | |
| "nullable": True | |
| } | |
| } | |
| output_type = "string" | |
| def forward(self, task_id: str, operation: str = "read") -> str: | |
| try: | |
| filename = f"{task_id}_downloaded_file" | |
| df = pd.read_excel(filename, engine="openpyxl") | |
| if operation == "read": | |
| return df.head().to_string() | |
| else: | |
| return f"Unsupported operation: {operation}" | |
| except Exception as e: | |
| return f"Error reading Excel file: {str(e)}" | |
| class ImageAnalysisTool(Tool): | |
| name = "ImageAnalysisTool" | |
| description = "Analyze a downloaded image file associated with a task ID using Claude Vision. Provide a detailed description of what's in the image." | |
| inputs = { | |
| "task_id": { | |
| "type": "string", | |
| "description": "Task ID for which the image file has been downloaded" | |
| }, | |
| "prompt": { | |
| "type": "string", | |
| "description": "Optional specific question or aspect to analyze about the image", | |
| "nullable": True | |
| } | |
| } | |
| output_type = "string" | |
| def __init__(self): | |
| super().__init__() | |
| self.client = anthropic.Client(api_key="") | |
| def forward(self, task_id: str, prompt: str = "Describe what you see in this image in detail.") -> str: | |
| try: | |
| filename = f"{task_id}_downloaded_file" | |
| with open(filename, 'rb') as img_file: | |
| img_bytes = img_file.read() | |
| img = Image.open(io.BytesIO(img_bytes)) | |
| if img.mode != 'RGB': | |
| img = img.convert('RGB') | |
| img_byte_arr = io.BytesIO() | |
| img.save(img_byte_arr, format='JPEG') | |
| img_byte_arr = img_byte_arr.getvalue() | |
| base64_image = base64.b64encode(img_byte_arr).decode('utf-8') | |
| message = self.client.messages.create( | |
| model="claude-3-7-sonnet-20250219", | |
| max_tokens=1000, | |
| messages=[{ | |
| "role": "user", | |
| "content": [ | |
| { | |
| "type": "image", | |
| "source": { | |
| "type": "base64", | |
| "media_type": "image/jpeg", | |
| "data": base64_image | |
| } | |
| }, | |
| { | |
| "type": "text", | |
| "text": prompt | |
| } | |
| ] | |
| }] | |
| ) | |
| return message.content[0].text | |
| except Exception as e: | |
| return f"Error analyzing image: {str(e)}" | |
| # New: TaskFileDownloaderTool | |
| class TaskFileDownloaderTool(Tool): | |
| name = "TaskFileDownloaderTool" | |
| description = "Download a specific file associated with a given task ID and save it locally" | |
| inputs = { | |
| "task_id": { | |
| "type": "string", | |
| "description": "Task ID for which to download the associated file" | |
| } | |
| } | |
| output_type = "string" | |
| def forward(self, task_id: str) -> str: | |
| try: | |
| download_url = f"{DEFAULT_API_URL}/files/{task_id}" | |
| response = requests.get(download_url) | |
| response.raise_for_status() | |
| filename = f"{task_id}_downloaded_file" | |
| with open(filename, "wb") as f: | |
| f.write(response.content) | |
| return f"File downloaded successfully and saved as: {filename}" | |
| except Exception as e: | |
| return f"Error downloading file: {str(e)}" | |
| # New: FileOpenerTool | |
| class FileOpenerTool(Tool): | |
| name = "FileOpenerTool" | |
| description = "Open a downloaded file associated with a task ID and read its contents as plain text." | |
| inputs = { | |
| "task_id": { | |
| "type": "string", | |
| "description": "Task ID for which the file has been downloaded" | |
| }, | |
| "num_lines": { | |
| "type": "integer", | |
| "description": "Number of lines to read from the file", | |
| "nullable": True | |
| } | |
| } | |
| output_type = "string" | |
| def forward(self, task_id: str, num_lines: int = 10) -> str: | |
| try: | |
| filename = f"{task_id}_downloaded_file" | |
| if not os.path.exists(filename): | |
| return f"Error: File {filename} does not exist." | |
| with open(filename, "r", encoding="utf-8", errors="ignore") as file: | |
| lines = [] | |
| for _ in range(num_lines): | |
| line = file.readline() | |
| if not line: | |
| break | |
| lines.append(line.strip()) | |
| return "\n".join(lines) | |
| except Exception as e: | |
| return f"Error reading file: {str(e)}" | |
| # New: SpeechToTextTool | |
| import mlx_whisper | |
| class SpeechToTextTool(Tool): | |
| name = "SpeechToTextTool" | |
| description = "Transcribe a downloaded MP3 audio file associated with a task ID into text." | |
| inputs = { | |
| "task_id": { | |
| "type": "string", | |
| "description": "Task ID for which the MP3 audio file has been downloaded" | |
| } | |
| } | |
| output_type = "string" | |
| def __init__(self): | |
| super().__init__() | |
| def forward(self, task_id: str) -> str: | |
| try: | |
| filename = f"{task_id}_downloaded_file" | |
| if not os.path.exists(filename): | |
| return f"Error: Audio file {filename} does not exist." | |
| result = mlx_whisper.transcribe(filename) | |
| return result["text"] | |
| except Exception as e: | |
| return f"Error transcribing audio file: {str(e)}" | |
| import wikipedia | |
| class WikipediaSearchTool(Tool): | |
| name = "WikipediaSearchTool" | |
| description = "Search Wikipedia for a query and return a brief summary." | |
| inputs = { | |
| "query": { | |
| "type": "string", | |
| "description": "Query to search on Wikipedia" | |
| } | |
| } | |
| output_type = "string" | |
| def __init__(self): | |
| super().__init__() | |
| wikipedia.set_lang("en") # Ensure English Wikipedia | |
| def forward(self, query: str) -> str: | |
| try: | |
| summary = wikipedia.summary(query, sentences=3000) | |
| return summary | |
| except wikipedia.exceptions.DisambiguationError as e: | |
| return f"Disambiguation error. Possible options: {e.options[:5]}" | |
| except wikipedia.exceptions.PageError: | |
| return f"Page not found for query: {query}" | |
| except Exception as e: | |
| return f"Error searching Wikipedia: {str(e)}" | |
| def format_transcript(transcript_data): | |
| return "\n".join([f"{item['start']}: {item['text']}" for item in transcript_data]) | |
| import os | |
| from youtube_transcript_api import YouTubeTranscriptApi | |
| import yt_dlp | |
| import mlx_whisper | |
| class YouTubeTranscriptTool(Tool): | |
| name = "YouTubeTranscriptTool" | |
| description = "Fetches or transcribes the text from a YouTube video ID." | |
| inputs = { | |
| "video_id": { | |
| "type": "string", | |
| "description": "YouTube Video ID (the part after 'watch?v=')" | |
| } | |
| } | |
| output_type = "string" | |
| def __init__(self): | |
| super().__init__() | |
| def forward(self, video_id: str) -> str: | |
| try: | |
| transcript_list = YouTubeTranscriptApi.list_transcripts(video_id) | |
| try: | |
| # First try manually created transcript | |
| transcript = transcript_list.find_manually_created_transcript(['en']) | |
| except Exception: | |
| # If not found, try auto-generated transcript | |
| transcript = transcript_list.find_generated_transcript(['en']) | |
| transcript_data = transcript.fetch() | |
| # Format nicely | |
| text = format_transcript(transcript_data) | |
| return text | |
| except Exception as e: | |
| print(f"No direct transcript found: {e}") | |
| print("Trying to download and transcribe audio with Whisper...") | |
| # Step 1: Download audio using yt_dlp | |
| audio_filename = f"{video_id}.mp3" | |
| try: | |
| ydl_opts = { | |
| 'format': 'bestaudio/best', | |
| 'outtmpl': audio_filename, | |
| 'postprocessors': [{ | |
| 'key': 'FFmpegExtractAudio', | |
| 'preferredcodec': 'mp3', | |
| 'preferredquality': '192', | |
| }], | |
| 'quiet': True, | |
| } | |
| with yt_dlp.YoutubeDL(ydl_opts) as ydl: | |
| ydl.download([f"https://www.youtube.com/watch?v={video_id}"]) | |
| # Step 2: Transcribe audio using mlx_whisper | |
| result = mlx_whisper.transcribe(audio_filename) | |
| return result["text"] | |
| except Exception as download_error: | |
| return f"Error downloading or transcribing YouTube audio: {str(download_error)}" | |
| finally: | |
| if os.path.exists(audio_filename): | |
| os.remove(audio_filename) # Clean up downloaded file | |
| # Load environment variables | |
| load_dotenv() | |
| # (Keep Constants as is) | |
| # --- Constants --- | |
| DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space" | |
| # --- Basic Agent Definition --- | |
| class BasicAgent: | |
| def __init__(self): | |
| print("Initializing Agent with tools...") | |
| # Initialize the model using Claude via LiteLLM | |
| self.model = LiteLLMModel( | |
| model_id="ollama_chat/qwen2:7b", | |
| api_base="http://127.0.0.1:11434", | |
| temperature=0.7, | |
| max_tokens=4096 | |
| ) | |
| # Initialize tools | |
| youtube_transcript_tool = YouTubeTranscriptTool() | |
| excel_tool = SimpleExcelTool() | |
| image_analysis_tool = ImageAnalysisTool() | |
| file_opener_tool = FileOpenerTool() | |
| speech_to_text_tool = SpeechToTextTool() | |
| task_file_downloader_tool = TaskFileDownloaderTool() | |
| wikipedia_search_tool = WikipediaSearchTool() | |
| self.tools = [ | |
| DuckDuckGoSearchTool(), | |
| wikipedia_search_tool, | |
| youtube_transcript_tool, | |
| PythonInterpreterTool(), | |
| excel_tool, | |
| image_analysis_tool, | |
| file_opener_tool, | |
| speech_to_text_tool, | |
| task_file_downloader_tool | |
| ] | |
| # Initialize the agent | |
| self.agent = CodeAgent( | |
| tools=self.tools, | |
| model=self.model, | |
| verbosity_level=LogLevel.INFO | |
| ) | |
| print("Agent initialized successfully") | |
| def __call__(self, question: str, task_id: str) -> str: | |
| print(f"Agent received question: {question[:100]}...") | |
| try: | |
| # Step 1: Download the file associated with the task first | |
| download_result = self.tools[-1](task_id=task_id) # TaskFileDownloaderTool is the last in self.tools | |
| print(download_result) | |
| # Step 2: Create a comprehensive prompt for the agent | |
| prompt = f"""Please answer the following question. Use the available tools (web search) | |
| to gather relevant information before providing a comprehensive answer. | |
| Question: {question} | |
| Task_id: {task_id} | |
| Instructions: | |
| 1. Search for relevant information using web search. | |
| 2. Look for relevant YouTube content if applicable. | |
| 3. If the task requires working with an Excel or image file: | |
| - First, download the file associated with the task ID using the file download tool. | |
| - Then, perform analysis on the downloaded file. | |
| 4. Extract and analyze data from Excel files after downloading. | |
| 5. Convert images to text after downloading the image file. | |
| 6. Convert attached mp3 to text as seepch to text | |
| 7. Make Wikipedia search on facts and for a query and return a brief summary | |
| 78. Synthesize all gathered and analyzed information into a clear, well-structured final answer. | |
| Answer:""" | |
| # Step 3: Get response from the agent | |
| response = self.agent.run(prompt) | |
| print(f"Agent generated response: {response[:100]}...") | |
| return response | |
| except Exception as e: | |
| error_msg = f"Error generating answer: {str(e)}" | |
| print(error_msg) | |
| return error_msg | |
| 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 | |
| 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() | |
| print(questions_data) | |
| 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 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, task_id) | |
| 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("# Advanced Agent Evaluation Runner") | |
| gr.Markdown( | |
| """ | |
| **Instructions:** | |
| 1. Make sure you have set up your environment variables: | |
| - HF_TOKEN: Your Hugging Face API token | |
| - YOUTUBE_API_KEY: Your YouTube API key (optional) | |
| 2. Log in to your Hugging Face account using the button below | |
| 3. Click 'Run Evaluation & Submit All Answers' to process all questions | |
| The agent will use: | |
| - Web search (DuckDuckGo) | |
| - YouTube search (if API key provided) | |
| - Mistral-7B-Instruct LLM | |
| """ | |
| ) | |
| 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 required environment variables | |
| hf_token = os.getenv("HF_TOKEN") | |
| if not hf_token: | |
| print("⚠️ Warning: HF_TOKEN not found in environment variables") | |
| youtube_api_key = os.getenv("YOUTUBE_API_KEY") | |
| if not youtube_api_key: | |
| print("ℹ️ Note: YOUTUBE_API_KEY not found. YouTube search will be disabled") | |
| # Check for SPACE_HOST and SPACE_ID | |
| 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?)") | |
| print("-"*(60 + len(" App Starting ")) + "\n") | |
| print("Launching Gradio Interface for Advanced Agent Evaluation...") | |
| demo.launch(debug=True, share=False) |