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| """ | |
| app.py | |
| This script provides the Gradio web interface to run the evaluation. | |
| This version properly handles multimodal inputs including images, videos, and audio. | |
| """ | |
| import os | |
| import re | |
| import gradio as gr | |
| import requests | |
| import pandas as pd | |
| from urllib.parse import urlparse | |
| from agent import create_agent_executor | |
| # --- Constants --- | |
| DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space" | |
| # --- Helper function to parse the agent's output --- | |
| def parse_final_answer(agent_response: str) -> str: | |
| match = re.search(r"FINAL ANSWER:\s*(.*)", agent_response, re.IGNORECASE | re.DOTALL) | |
| if match: return match.group(1).strip() | |
| lines = [line for line in agent_response.split('\n') if line.strip()] | |
| if lines: return lines[-1].strip() | |
| return "Could not parse a final answer." | |
| def detect_file_type(url: str) -> str: | |
| """Detect the type of file from URL.""" | |
| if not url: | |
| return "unknown" | |
| url_lower = url.lower() | |
| # Image extensions | |
| if any(ext in url_lower for ext in ['.jpg', '.jpeg', '.png', '.gif', '.bmp', '.webp', '.svg']): | |
| return "image" | |
| # Video extensions and YouTube | |
| if any(domain in url_lower for domain in ['youtube.com', 'youtu.be', 'vimeo.com']): | |
| return "youtube" | |
| if any(ext in url_lower for ext in ['.mp4', '.avi', '.mov', '.wmv', '.flv', '.webm']): | |
| return "video" | |
| # Audio extensions | |
| if any(ext in url_lower for ext in ['.mp3', '.wav', '.flac', '.aac', '.ogg', '.m4a']): | |
| return "audio" | |
| # Try to detect from headers if possible | |
| try: | |
| response = requests.head(url, timeout=5) | |
| content_type = response.headers.get('content-type', '').lower() | |
| if 'image' in content_type: | |
| return "image" | |
| elif 'audio' in content_type: | |
| return "audio" | |
| elif 'video' in content_type: | |
| return "video" | |
| except: | |
| pass | |
| return "unknown" | |
| def create_enhanced_prompt(question_text: str, file_url: str = None) -> str: | |
| """Create an enhanced prompt that guides the agent to use appropriate tools.""" | |
| if not file_url: | |
| return question_text | |
| file_type = detect_file_type(file_url) | |
| if file_type == "image": | |
| return f"""{question_text} | |
| [IMAGE ATTACHMENT]: {file_url} | |
| INSTRUCTION: There is an image attached to this question. You MUST use the 'describe_image' tool to analyze this image before answering the question.""" | |
| elif file_type == "youtube": | |
| return f"""{question_text} | |
| [YOUTUBE VIDEO]: {file_url} | |
| INSTRUCTION: There is a YouTube video attached to this question. You MUST use the 'process_youtube_video' tool to analyze this video before answering the question.""" | |
| elif file_type == "audio": | |
| return f"""{question_text} | |
| [AUDIO FILE]: {file_url} | |
| INSTRUCTION: There is an audio file attached to this question. You MUST use the 'process_audio_file' tool to analyze this audio before answering the question.""" | |
| else: | |
| return f"""{question_text} | |
| [ATTACHMENT]: {file_url} | |
| INSTRUCTION: There is a file attachment. Analyze the URL and use the appropriate tool to process this content before answering the question.""" | |
| def run_and_submit_all(profile: gr.OAuthProfile | None): | |
| """ | |
| Fetches all questions, runs the agent on them, submits all answers, | |
| and displays the results. | |
| """ | |
| if not profile: | |
| return "Please log in to Hugging Face with the button above to submit.", None | |
| username = profile.username | |
| print(f"User logged in: {username}") | |
| space_id = os.getenv("SPACE_ID") | |
| agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" | |
| questions_url = f"{DEFAULT_API_URL}/questions" | |
| submit_url = f"{DEFAULT_API_URL}/submit" | |
| # 1. Instantiate Agent | |
| print("Initializing your custom agent...") | |
| try: | |
| agent_executor = create_agent_executor(provider="google") # Using Google for better multimodal support | |
| except Exception as e: | |
| return f"Fatal Error: Could not initialize agent. Check logs. Details: {e}", None | |
| # 2. Fetch Questions | |
| print(f"Fetching questions from: {questions_url}") | |
| try: | |
| response = requests.get(questions_url, timeout=20) | |
| response.raise_for_status() | |
| questions_data = response.json() | |
| print(f"Fetched {len(questions_data)} questions.") | |
| except Exception as e: | |
| return f"Error fetching questions: {e}", pd.DataFrame() | |
| # 3. Run your Agent | |
| results_log, answers_payload = [], [] | |
| print(f"Running agent on {len(questions_data)} questions...") | |
| for i, item in enumerate(questions_data): | |
| task_id = item.get("task_id") | |
| question_text = item.get("question") | |
| if not task_id or question_text is None: | |
| continue | |
| print(f"\n--- Running Task {i+1}/{len(questions_data)} (ID: {task_id}) ---") | |
| # Get file URL if it exists | |
| file_url = item.get("file_url") | |
| # Create enhanced prompt that instructs the agent to use appropriate tools | |
| full_question_text = create_enhanced_prompt(question_text, file_url) | |
| if file_url: | |
| file_type = detect_file_type(file_url) | |
| print(f"File detected: {file_url} (Type: {file_type})") | |
| print(f"Enhanced Prompt for Agent:\n{full_question_text}") | |
| try: | |
| # Pass the enhanced question to the agent | |
| result = agent_executor.invoke({"messages": [("user", full_question_text)]}) | |
| raw_answer = result['messages'][-1].content | |
| submitted_answer = parse_final_answer(raw_answer) | |
| print(f"Raw LLM Response: '{raw_answer}'") | |
| print(f"PARSED FINAL ANSWER: '{submitted_answer}'") | |
| answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer}) | |
| results_log.append({ | |
| "Task ID": task_id, | |
| "Question": question_text, | |
| "File URL": file_url or "None", | |
| "File Type": detect_file_type(file_url) if file_url else "None", | |
| "Submitted Answer": submitted_answer | |
| }) | |
| except Exception as e: | |
| print(f"!! AGENT ERROR on task {task_id}: {e}") | |
| error_msg = f"AGENT RUNTIME ERROR: {e}" | |
| answers_payload.append({"task_id": task_id, "submitted_answer": error_msg}) | |
| results_log.append({ | |
| "Task ID": task_id, | |
| "Question": question_text, | |
| "File URL": file_url or "None", | |
| "File Type": detect_file_type(file_url) if file_url else "None", | |
| "Submitted Answer": error_msg | |
| }) | |
| if not answers_payload: | |
| return "Agent did not produce any answers to submit.", pd.DataFrame(results_log) | |
| # 4. Prepare and 5. Submit | |
| submission_data = {"username": username, "agent_code": agent_code, "answers": answers_payload} | |
| print(f"\nSubmitting {len(answers_payload)} answers for user '{username}'...") | |
| try: | |
| response = requests.post(submit_url, json=submission_data, timeout=60) | |
| response.raise_for_status() | |
| result_data = response.json() | |
| final_status = (f"Submission Successful!\nUser: {result_data.get('username')}\n" | |
| f"Overall Score: {result_data.get('score', 'N/A')}%\n" | |
| f"Processed {len([r for r in results_log if 'ERROR' not in r['Submitted Answer']])} successful tasks") | |
| return final_status, pd.DataFrame(results_log) | |
| except Exception as e: | |
| status_message = f"Submission Failed: {e}" | |
| print(status_message) | |
| return status_message, pd.DataFrame(results_log) | |
| # --- Gradio UI --- | |
| with gr.Blocks(title="Multimodal Agent Evaluation") as demo: | |
| gr.Markdown("# Multimodal Agent Evaluation Runner") | |
| gr.Markdown("This agent can process images, YouTube videos, audio files, and perform web searches.") | |
| gr.LoginButton() | |
| run_button = gr.Button("Run Evaluation & Submit All Answers", variant="primary") | |
| status_output = gr.Textbox(label="Run Status / Submission Result", lines=6, interactive=False) | |
| results_table = gr.DataFrame( | |
| label="Questions and Agent Answers", | |
| wrap=True, | |
| row_count=10, | |
| column_widths=[80, 200, 150, 80, 200] | |
| ) | |
| run_button.click(fn=run_and_submit_all, outputs=[status_output, results_table]) | |
| if __name__ == "__main__": | |
| print("\n" + "-"*30 + " Multimodal App Starting " + "-"*30) | |
| demo.launch() |