Upload two files: agent.py and run.py [remove the agent class]

agent.py

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+import os
+from typing import TypedDict, List, Dict, Any, Optional
+from langgraph.graph import StateGraph, START, END
+from langchain_anthropic import ChatAnthropic
+from langchain_core.tools import tool
+from langchain_core.messages import HumanMessage
+from langchain_core.prompts import ChatPromptTemplate
+
+# %pip install -qU duckduckgo-search langchain-community
+# pip install requests
+# pip install pandas
+# pip install pypdf
+
+
+class AgentState(TypedDict):
+    messages: List
+    current_question: str
+    final_answer: str
+
+# 1. Web Browsing
+from langchain_community.tools import DuckDuckGoSearchRun
+from langchain_community.document_loaders import ImageCaptionLoader
+import requests
+import pandas as pd
+from pypdf import PdfReader
+
+@tool
+def web_search(query: str) -> str:
+    """Allows search through DuckDuckGo.
+    Args:
+        query: what you want to search
+    """
+    search = DuckDuckGoSearchRun()
+    results = search.invoke(query)
+    return "\n".join(results)
+
+@tool
+def visit_webpage(url: str) -> str:
+    """Fetches raw HTML content of a web page.
+    Args:
+        url: the webpage url
+    """
+    try:
+        response = requests.get(url, timeout=5)
+        return response.text
+    except Exception as e:
+        return f"[ERROR fetching {url}]: {str(e)}"
+
+# 4. File Reading
+@tool
+def read_file(dir: str) -> str:
+    """Read the content of the provided file
+    Args:
+        dir: the filepath
+    """
+    extension = dir.split['.'][-1]
+    if extension == 'xlsx':
+        dataframe = pd.read_excel(dir)
+        return dataframe.to_string()
+    elif extension == 'pdf':
+        reader = PdfReader(dir)
+        contents = [p.extract_text() for p in reader.pages]
+        return "\n".join(contents)
+    else:
+        with open(dir) as f:
+            return f.read()
+
+# 5. Image Open
+@tool
+def image_caption(dir: str) -> str:
+    """Understand the content of the provided image
+    Args:
+        dir: the image url link
+    """
+    loader = ImageCaptionLoader(images=[dir])
+    metadata = loader.load()
+    return metadata[0].page_content
+
+# 2. Coding
+# 3. Multi-Modality
+
+
+
+# ("human", f"Question: {question}\nReport to validate: {final_answer}")
+class BasicAgent:
+    def __init__(self):
+        model = ChatAnthropic(
+            model="claude-3-5-sonnet-20240620",
+            temperature=0,
+            max_tokens=20000,
+            timeout=None,
+            max_retries=2,
+            api_key=os.getenv("ANTHROPIC_API_KEY"),
+            # other params...
+        )
+        # System Prompt for few shot prompting
+        self.sys_prompt = """"
+                You are a general AI assistant. I will ask you a question. Report your thoughts, and finish your answer with the following template: FINAL ANSWER: [YOUR FINAL ANSWER].
+                YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separared list of numbers and/or strings.
+                If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise.
+                If you are asked for a string, don't use articles, neither abbreviations (eg. for cities), and write the digits in plain text unless specified otherwise.
+                If you are asked for a comma separated list, apply the above rules depending of whether the element to put in the list is a number or a string.
+
+                There are few tools provided: web_search, visit_webpage, read_file and image_caption. 
+                Here are few examples demonstrating how to call and use the tools.
+        """
+        self.app = self.__graph_compile__()
+        tools = [web_search, visit_webpage, read_file, image_caption]
+        self.model = model.bind_tools(tools) # LLM with tools
+        # self.agent = CodeAgent(tools=[DuckDuckGoSearchTool()], model=model)
+        print("BasicAgent initialized.")
+    
+    def __call__(self, question: str) -> str:
+        print(f"Agent received question (first 50 chars): {question[:50]}...")
+        prompt_msg = [
+            ("system", self.sys_prompt),
+            ("human", f"Question: {question}")
+        ]
+        response = self.model.invoke(prompt_msg)
+        fixed_answer = response.content
+        # fixed_answer = "This is a default answer."
+        print(f"Agent returning fixed answer: {fixed_answer}")
+        return fixed_answer
+    
+    # Maybe we no need this one
+    def __graph_compile__(self):
+        graph = StateGraph(AgentState)
+
+        pass

run.py

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+import os
+import gradio as gr
+import requests
+import inspect
+import pandas as pd
+
+# (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 ------
+# from smolagents import CodeAgent, DuckDuckGoSearchTool, OpenAIServerModel, VLLMModel, HfApiModel
+# class BasicAgent:
+#     def __init__(self):
+#         # model = OpenAIServerModel(model_id="gpt-4o")
+#         # model = VLLMModel(model_id="HuggingFaceTB/SmolLM-135M-Instruct")
+#         model = HfApiModel("Qwen/Qwen2.5-Coder-32B-Instruct")
+#         self.agent = CodeAgent(tools=[DuckDuckGoSearchTool()], model=model)
+#         print("BasicAgent initialized.")
+#     def __call__(self, question: str) -> str:
+#         print(f"Agent received question (first 50 chars): {question[:50]}...")
+#         fixed_answer = self.agent.run(question)
+#         # fixed_answer = "This is a default answer."
+#         print(f"Agent returning fixed answer: {fixed_answer}")
+#         return fixed_answer
+from agent import BasicAgent
+
+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
+
+
+# --- 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)