Update app.py

app.py

@@ -1,315 +1,218 @@
-#pip install langchain_google_genai langgraph gradio
 import os
-import sys
-import typing
-from typing import Annotated, Literal, Iterable
-from typing_extensions import TypedDict
-
+import inspect
 from langchain_google_genai import ChatGoogleGenerativeAI
-from langgraph.graph import StateGraph, START, END
-from langgraph.graph.message import add_messages
-from langgraph.prebuilt import ToolNode
-from langchain_core.tools import tool
-from langchain_core.messages import AIMessage, ToolMessage, HumanMessage, BaseMessage, SystemMessage
-from random import randint
-
-import requests
-from bs4 import BeautifulSoup
-import openpyxl
-import wikipedia
-import pandas as pd
-
-import gradio as gr
-import logging
-
-class OrderState(TypedDict):
-    """State representing the customer's order conversation."""
-    messages: Annotated[list, add_messages]
-    order: list[str]
-    finished: bool
+from GenericAgent import AgenticAI
 
-# System instruction for the Agent
-SYSINT = (
-    "system",
-    "You are a general AI assistant. I will ask you a question."
-    "The question requires a tool to solve. You must attempt to use at least one of the available tools before returning an answer."
-    "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 separated 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 (e.g. 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 be put in the list is a number or a string."
-    "If a tool required for task completion is not functioning, return 0."
-    )
+# (Keep Constants as is)
+# --- Constants ---
+DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-WELCOME_MSG = "Welcome to my general-purpose AI agent. Type `q` to quit. How shall I fail to serve you today?"
+# --- Basic Agent Definition ---
+# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
+class BasicAgent:
+    def __init__(self):
+        print("BasicAgent initialized.")
+        self.agent = AgenticAI()
 
-# Initialize the Google Gemini LLM
-llm = ChatGoogleGenerativeAI(model="gemini-1.5-flash-latest")
+    def summarize(self, question: str) -> str:
+        prompt = """You are an AI assistant for summarizing tasks. You will receive a long request message, your task is to make it shorter by removing irrelevant details. Do no attempt to find an answer to the request or any part of the request. Make sure to always include any requirement towards the answer's format in your summary.
+        
+        EXAMPLE:
+        'Hi, we've been learning about reptiles in biology and I'd like to know more about crocodiles. Can you tell me how many legs an average crocodile has? Please answer only with a number! Thanks in advance!' 
+        'How many legs does an average crocodile have? Answer with only a number.'
+        
+        REQUEST:
+        """
+        llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash")
+        summary = llm.invoke(prompt+question)
+        return summary
+    
+    def __call__(self, question: str) -> str:
+        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 separated 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 (e.g. 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 be put in the list is a number or a string. If a tool required for task completion is unavailable after multiple tries, return 0.
+        
+        QUESTION:
+        """
+        print(f"Agent received question (first 50 chars): {question[:50]}...")
 
-@tool
-def wikipedia_search_tool(title: str) -> str:
-    """Provides an excerpt from a Wikipedia article with the given title."""
-    page = wikipedia.page(title, auto_suggest=False)
-    return page.content[:3000]
+        if len(question) > 400:
+            question = self.summary(question)
 
-@tool
-def media_tool(file_path: str) -> str:
-    """Used for deciphering video and audio files."""
-    return "This tool hasn't been implemented yet. Please return 0 if the task cannot be solved without knowing the contents of this file."
+        answer = self.agent.ask(prompt + question)
+        print(f"Agent returning answer: {str(answer)}")
+        return str(answer).split("FINAL ANSWER:")[-1].strip()
 
-@tool
-def internet_search_tool(search_query: str) -> str:
-    """Does a google search with using the input as the search query. Returns a long batch of textual information related to the query."""
-    search_tool = DuckDuckGoSearchTool()
-    result = search_tool(search_query)
-    return result
+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
 
-@tool
-def webscraper_tool(url: str) -> str:
-    """Returns the page's html content from the input url."""
-    response = requests.get(url, stream=True)
-    if response.status_code == 200:
-        soup = BeautifulSoup(response.content, 'html.parser')
-        html_text = soup.get_text()
-        return html_text
+    if profile:
+        username= f"{profile.username}"
+        print(f"User logged in: {username}")
     else:
-        raise Exception(f"Failed to retrieve the webpage. Status code: {response.status_code}")
-
-@tool
-def read_excel_tool(file_path: str) -> str:
-    """Returns the contents of an Excel file as a Pandas dataframe."""
-    df = pd.read_excel(file_path, engine = "openpyxl")
-    return df
+        print("User not logged in.")
+        return "Please Login to Hugging Face with the button.", None
 
-def agent_node(state: OrderState) -> OrderState:
-    """agent with tool handling."""
-    print(f"Messagelist sent to agent node: {[msg.content for msg in state.get('messages', [])]}")
-    defaults = {"order": [], "finished": False}
-
-    # Ensure we always have at least a system message
-    if not state.get("messages", []):
-        return defaults | state | {"messages": [SystemMessage(content=SYSINT), new_output]}
+    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:
-        # Prepend system instruction if not already present
-        messages_with_system = [
-            SystemMessage(content=SYSINT)
-        ] + state.get("messages", [])
-
-        # Process messages through the LLM
-        new_output = llm_with_tools.invoke(messages_with_system)
-
-        return defaults | state | {"messages": [new_output]}
+        agent = BasicAgent()
     except Exception as e:
-        # Fallback if LLM processing fails
-        return defaults | state | {"messages": [AIMessage(content=f"I'm having trouble processing that. {str(e)}")]}
-
-def interactive_tools_node(state: OrderState) -> OrderState:
-    """Handles interactive tool calls."""
-    logging.info("interactive tools node")
-    tool_msg = state.get("messages", [])[-1]
-    order = state.get("order", [])
-    outbound_msgs = []
-
-    for tool_call in tool_msg.tool_calls:
-        tool_name = tool_call["name"]
-        tool_args = tool_call["args"]
-
-        if tool_name == "wikipedia_search_tool":
-            print(f"called wikipedia with {str(tool_args)}")
-            page = wikipedia.page(tool_args.get("title"), auto_suggest=False)
-            response = page.content[:3000]
-        elif tool_name == "media_tool":
-            print(f"called media with {str(tool_args)}")
-            response = "This tool hasn't been implemented yet. Please return 0 if the task cannot be solved without knowing the contents of this file."
-        elif tool_name == "internet_search_tool":
-            print(f"called internet with {str(tool_args)}")
-            question = tool_args.get("search_query")
-            search_tool = DuckDuckGoSearchTool()
-            response = search_tool(question)[:3000]
-        elif tool_name == "webscraper_tool":
-            print(f"called webscraper with {str(tool_args)}")
-            url = tool_args.get("url")
-            response = requests.get(url, stream=True)
-            if response.status_code == 200:
-                soup = BeautifulSoup(response.content, 'html.parser')
-                html_text = soup.get_text()
-                response = html_text
-            else:
-                response = Exception(f"Failed to retrieve the webpage. Status code: {response.status_code}")
-        elif tool_name == "read_excel_tool":
-            print(f"called excel with {str(tool_args)}")
-            path = tool_args.get("file_path")
-            df = pd.read_excel(path, engine = "openpyxl")
-            response = df
-
-        else:
-            raise NotImplementedError(f'Unknown tool call: {tool_name}')
-
-        outbound_msgs.append(
-            ToolMessage(
-                content=response,
-                name=tool_name,
-                tool_call_id=tool_call["id"],
-            )
+        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.
+        """
+    )
 
-    return {"messages": outbound_msgs, "order": order, "finished": False}
-    
-def maybe_route_to_tools(state: OrderState) -> str:
-    """Route between chat and tool nodes."""
-    if not (msgs := state.get("messages", [])):
-        raise ValueError(f"No messages found when parsing state: {state}")
-
-    msg = msgs[-1]
-
-    if state.get("finished", False):
-        print("from agent GOTO End node")
-        return END
-
-    elif hasattr(msg, "tool_calls") and len(msg.tool_calls) > 0:
-        if any(tool["name"] in tool_node.tools_by_name.keys() for tool in msg.tool_calls):
-            print("from agent GOTO tools node")
-            return "tools"
-        else:
-            logging.info("from chatbot GOTO interactive tools node")
-            return "interactive_tools"
-
-    print("tool call failed, quitting")
-    return "human"
+    gr.LoginButton()
 
-def human_node(state: OrderState) -> OrderState:
-    """Handle user input."""
-    logging.info(f"Messagelist sent to human node: {[msg.content for msg in state.get('messages', [])]}")
-    last_msg = state["messages"][-1]
+    run_button = gr.Button("Run Evaluation & Submit All Answers")
 
-    if last_msg.content.lower() in {"q", "quit", "exit", "goodbye"}:
-        state["finished"] = True
+    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)
 
-    return state
+    run_button.click(
+        fn=run_and_submit_all,
+        outputs=[status_output, results_table]
+    )
 
-def maybe_exit_human_node(state: OrderState) -> Literal["agent", "__end__"]:
-    """Determine if conversation should continue."""
-    if state.get("finished", False):
-        logging.info("from human GOTO End node")
-        return END
-    last_msg = state["messages"][-1]
-    if isinstance(last_msg, AIMessage):
-        logging.info("Chatbot response obtained, ending conversation")
-        return END
+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:
-        logging.info("from human GOTO agent node")
-        return "agent"
-
-# Prepare tools
-auto_tools = []
-tool_node = ToolNode(auto_tools)
-
-interactive_tools = [wikipedia_search_tool, media_tool, internet_search_tool, webscraper_tool, read_excel_tool]
-
-# Bind all tools to the LLM
-llm_with_tools = llm.bind_tools(auto_tools + interactive_tools)
-
-# Build the graph
-graph_builder = StateGraph(OrderState)
-
-# Add nodes
-graph_builder.add_node("agent", agent_node)
-graph_builder.add_node("human", human_node)
-graph_builder.add_node("tools", tool_node)
-graph_builder.add_node("interactive_tools", interactive_tools_node)
-
-# Add edges and routing
-graph_builder.add_conditional_edges("agent", maybe_route_to_tools)
-graph_builder.add_conditional_edges("human", maybe_exit_human_node)
-graph_builder.add_edge("tools", "agent")
-graph_builder.add_edge("interactive_tools", "agent")
-graph_builder.add_edge(START, "human")
-
-# Compile the graph
-chat_graph = graph_builder.compile()
-
-def convert_history_to_messages(history: list) -> list[BaseMessage]:
-    """
-    Convert Gradio chat history to a list of Langchain messages.
-    
-    Args:
-    - history: Gradio's chat history format
-    
-    Returns:
-    - List of Langchain BaseMessage objects
-    """
-    messages = []
-    for human, ai in history:
-        if human:
-            messages.append(HumanMessage(content=human))
-        if ai:
-            messages.append(AIMessage(content=ai))
-    return messages
+        print("ℹ️  SPACE_HOST environment variable not found (running locally?).")
 
-def gradio_chat(message: str, history: list) -> str:
-    """
-    Gradio-compatible chat function that manages the conversation state.
-    
-    Args:
-    - message: User's input message
-    - history: Gradio's chat history
-    
-    Returns:
-    - Bot's response as a string
-    """
-    logging.info(f"{len(history)} history so far: {history}")
-    # Ensure non-empty message
-    if not message or message.strip() == "":
-        message = "Hello, how can I help you today?"
-
-    # Convert history to Langchain messages
-    conversation_messages = []
-    for old_message in history:
-        if old_message["content"].strip():
-            if old_message["role"] == "user":
-                conversation_messages.append(HumanMessage(content=old_message["content"]))
-            if old_message["role"] == "assistant":
-                conversation_messages.append(AIMessage(content=old_message["content"]))
-    
-    # Add current message
-    conversation_messages.append(HumanMessage(content=message))
-    
-    # Create initial state with conversation history
-    conversation_state = {
-        "messages": conversation_messages, 
-        "order": [], 
-        "finished": False
-    }
-    logging.info(f"Conversation so far: {str(conversation_state)}")
-    try:
-        # Process the conversation through the graph
-        conversation_state = chat_graph.invoke(conversation_state, {"recursion_limit": 10})
-        
-        # Extract the latest bot message
-        latest_message = conversation_state["messages"][-1]
-        
-        # Return the bot's response content
-        logging.info(f"return: {latest_message.content}")
-        return latest_message.content
-    
-    except Exception as e:
-        return f"An error occurred: {str(e)}"
+    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.")
 
-# Gradio interface
-def launch_agent():
-    gr.ChatInterface(
-        gradio_chat,
-        type="messages",
-        title="Agent",
-        description="An AI agent (work in progress)",
-        theme="ocean"
-    ).launch()
+    print("-"*(60 + len(" App Starting ")) + "\n")
 
-if __name__ == "__main__":
-    # initiate logging tool
-    logging.basicConfig(
-                    stream=sys.stdout,
-                    level=logging.INFO,
-                    format='%(asctime)s - %(levelname)s - %(message)s')
-    launch_agent()
+    print("Launching Gradio Interface for Basic Agent Evaluation...")
+    demo.launch(debug=True, share=False)