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Update app.py
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app.py
CHANGED
@@ -3,21 +3,78 @@ import gradio as gr
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import requests
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import inspect
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import pandas as pd
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# (Keep Constants as is)
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# --- Constants ---
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DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
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# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
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class BasicAgent:
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def __init__(self):
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print("
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print(f"Agent received question (first 50 chars): {question[:50]}...")
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def run_and_submit_all( profile: gr.OAuthProfile | None):
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"""
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@@ -36,11 +93,12 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
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api_url = DEFAULT_API_URL
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questions_url = f"{api_url}/questions"
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submit_url = f"{api_url}/submit"
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# 1. Instantiate Agent ( modify this part to create your agent)
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try:
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agent =
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except Exception as e:
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print(f"Error instantiating agent: {e}")
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return f"Error initializing agent: {e}", None
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@@ -76,20 +134,45 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
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for item in questions_data:
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task_id = item.get("task_id")
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question_text = item.get("question")
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if not task_id or question_text is None:
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print(f"Skipping item with missing task_id or question: {item}")
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continue
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try:
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answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
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results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
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except Exception as e:
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print(f"Error running agent on task {task_id}: {e}")
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results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})
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if not answers_payload:
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print("Agent did not produce any answers to submit.")
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return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
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# 4. Prepare Submission
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submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
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import requests
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import inspect
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import pandas as pd
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import time
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from smolagents import CodeAgent, WikipediaSearchTool, DuckDuckGoSearchTool, OpenAIServerModel
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from PIL import Image
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from io import BytesIO
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# (Keep Constants as is)
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# --- Constants ---
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DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
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GEMINI_API_KEY = os.environ.get("GEMINI_API_KEY")
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def is_valid_image_pillow(file_name):
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try:
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with Image.open(file_name) as img:
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img.verify() # Verify the image file
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return True
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except (IOError, SyntaxError):
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return False
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class myAgent:
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def __init__(self):
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print("myAgent initialized.")
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self.agent = CodeAgent(
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model = OpenAIServerModel(
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model_id="gemini-2.0-flash-lite",
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api_base="https://generativelanguage.googleapis.com/v1beta/openai/",
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api_key=GEMINI_API_KEY,
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),
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tools=[DuckDuckGoSearchTool(), WikipediaSearchTool()],
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add_base_tools=True,
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# additional_authorized_imports=['pandas','numpy','csv']
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)
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def __call__(self, question: str, file_data=None) -> str: # Renamed img to file_data
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print(f"Agent received question (first 50 chars): {question[:50]}...")
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images_for_agent = [] # List to hold image objects
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text_from_file = "" # String to hold text content from files
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if file_data:
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print(f"Agent received file data of size: {len(file_data)} bytes")
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# Attempt to open as an image
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try:
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img_obj = Image.open(BytesIO(file_data))
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img_obj.verify() # Verify if it's a valid image
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images_for_agent.append(img_obj)
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print("File identified as an image.")
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except (IOError, SyntaxError):
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print("File is not an image, attempting to decode as text.")
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# If not an image, try to decode as text
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try:
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text_from_file = file_data.decode('utf-8')
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print(f"File decoded as text (first 200 chars): {text_from_file[:200]}...")
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except UnicodeDecodeError:
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text_from_file = f"Could not decode file as UTF-8 text. Raw bytes size: {len(file_data)}"
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print("File could not be decoded as UTF-8 text.")
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except Exception as e:
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print(f"Unexpected error processing file data: {e}")
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text_from_file = f"Error processing file: {e}"
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# Combine question with file content if available
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if text_from_file:
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# You might want to prepend or append, or format this more intelligently
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question_with_file_context = f"{question}\n\n[FILE CONTENT START]\n{text_from_file}\n[FILE CONTENT END]"
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else:
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question_with_file_context = question
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# Pass images and the possibly augmented question to the CodeAgent
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answer = self.agent.run(question_with_file_context, images=images_for_agent if images_for_agent else None)
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time.sleep(5)
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print(f"Agent returning answer: {answer}")
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return answer
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def run_and_submit_all( profile: gr.OAuthProfile | None):
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"""
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api_url = DEFAULT_API_URL
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questions_url = f"{api_url}/questions"
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files_url = f"{api_url}/files"
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submit_url = f"{api_url}/submit"
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# 1. Instantiate Agent ( modify this part to create your agent)
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try:
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agent = myAgent()
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except Exception as e:
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print(f"Error instantiating agent: {e}")
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return f"Error initializing agent: {e}", None
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for item in questions_data:
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task_id = item.get("task_id")
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question_text = item.get("question")
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file_name = item.get("file_name")
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file_content_to_pass = None # Initialize to None
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if file_name:
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# Fetch files
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print(f"Fetching file '{file_name}' for task_id: {task_id}")
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try:
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response = requests.get(f'{files_url}/{task_id}', timeout=15, allow_redirects=True)
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print("Response status code:", response.status_code)
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if response.status_code == 404:
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print(f"File not found for task_id {task_id}. Skipping file processing for this task.")
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# Continue without a file, agent will still receive the question
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else:
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response.raise_for_status()
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file_content_to_pass = response.content # Store the raw content
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print(f"Fetched file for task_id {task_id}: {file_name} (size: {len(file_content_to_pass)} bytes)")
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except requests.exceptions.RequestException as e:
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print(f"Error fetching file for task {task_id}: {e}. Agent will run without file.")
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# Do not return here, allow agent to run with just the question if file fetch fails
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except Exception as e:
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print(f"An unexpected error occurred fetching file for task {task_id}: {e}. Agent will run without file.")
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# Do not return here, allow agent to run with just the question if file fetch fails
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if not task_id or question_text is None:
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print(f"Skipping item with missing task_id or question: {item}")
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continue
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try:
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# Pass file_content_to_pass to the agent.
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# Your agent's __call__ method needs to be ready to handle
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# raw byte content for the 'img' parameter, or you might
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# rename it to something more generic like 'file_data'.
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submitted_answer = agent(question_text, file_content_to_pass)
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answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
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results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer, "File Name": file_name if file_name else "N/A"})
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except Exception as e:
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print(f"Error running agent on task {task_id}: {e}")
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results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}", "File Name": file_name if file_name else "N/A"})
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# 4. Prepare Submission
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submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
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