app.py

# 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 ------
# class BasicAgent:
#     def __init__(self):
#         print("BasicAgent initialized.")
#     def __call__(self, question: str) -> str:
#         print(f"Agent received question (first 50 chars): {question[:50]}...")
#         fixed_answer = "This is a default answer."
#         print(f"Agent returning fixed answer: {fixed_answer}")
#         return fixed_answer

# 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)

##################################
#
# =================================================================================================
#  ✅ --- ✅  FINAL ASSESSMENT AGENT - INSTRUCTOR'S VERSION ✅ --- ✅
# =================================================================================================
#
#  Instructions:
#  1. Make sure you have a requirements.txt file with all the necessary packages.
#  2. Set your GROQ_API_KEY in the Hugging Face Space secrets.
#  3. This code replaces the original template entirely.
#
# =================================================================================================
# =================================================================================================
#  ✅ --- ✅  FINAL ASSESSMENT AGENT - INSTRUCTOR'S CORRECTED VERSION ✅ --- ✅
# =================================================================================================
#
#  Instructions:
#  1. Make sure your requirements.txt file matches the one provided by the instructor.
#  2. Set your GROQ_API_KEY in the Hugging Face Space secrets.
#  3. This code replaces the original template entirely.
#
# =================================================================================================

# 

###########################
# =================================================================================================
#  ✅ --- ✅  FINAL ASSESSMENT AGENT - V4 (STATE-FIXED & TAVILY) ✅ --- ✅
# =================================================================================================
#
#  Instructions:
#  1. Add TAVILY_API_KEY and GROQ_API_KEY to your HF Space secrets.
#  2. Update your requirements.txt to include `tavily-python`.
#  3. This version fixes the critical state-leakage bug and uses a better search tool.
#
# =================================================================================================

# 

######################
# =================================================================================================
#  ✅ --- ✅  FINAL ASSESSMENT AGENT - V5 (GPT-4o & PDF Support) ✅ --- ✅
# =================================================================================================
#
#  Instructions:
#  1. Add OPENAI_API_KEY, TAVILY_API_KEY, and GROQ_API_KEY to your HF Space secrets.
#  2. Update your requirements.txt to include `langchain-openai` and `pypdf`.
#  3. This version uses the GPT-4o model for superior reasoning and can read PDFs.
#
# =================================================================================================

# 
import os
import io
import requests
import pandas as pd
import gradio as gr
from contextlib import redirect_stdout
from typing import List

# --- LangChain & LangGraph Imports ---
from langchain_core.messages import BaseMessage
from langchain_core.tools import tool
from langchain_cohere.chat_models import ChatCohere
from langchain.agents import AgentExecutor
from langchain_core.prompts import ChatPromptTemplate
# These are the fundamental components we need for a Cohere Tools agent
from langchain.agents.format_scratchpad.cohere import format_cohere_tools
from langchain.agents.output_parsers.cohere import CohereToolsAgentOutputParser


from tavily import TavilyClient
import pypdf

# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
FILES_DIR = "./files"
os.makedirs(FILES_DIR, exist_ok=True)

# --- System Prompt (Unchanged) ---
AGENT_SYSTEM_PROMPT = """You are a world-class AI agent, specialized in solving complex problems from the GAIA benchmark.
Your task is to analyze the user's question, think step-by-step, and use the provided tools to find the correct answer.
CRITICAL INSTRUCTIONS:
1.  **Analyze the Goal:** First, understand what the user is asking for.
2.  **Plan & Execute:** Formulate a plan and use the available tools (`tavily_search`, `read_file`, `python_interpreter`) to gather information.
3.  **Final Answer Format:** Once you are absolutely certain of the answer, you MUST provide it directly and concisely.
    - DO NOT include your reasoning, thoughts, or any conversational text like 'The answer is...', 'Here is the result:', or 'Based on my search...'.
    - Your final response must ONLY be the answer itself.
EXAMPLES OF CORRECT FINAL ANSWERS:
- If the question asks for a year: `2023`
- If it asks for a name: `John Doe`
- If it asks for a number: `42`
- If it asks for a comma-separated list: `item1, item2, item3`
Think, use your tools, and then provide ONLY the final, precise answer.
"""

#
# ================================================================================================
#  ✅ 1. DEFINE THE AGENT'S TOOLS (Unchanged)
# ================================================================================================
#
tavily = TavilyClient(api_key=os.getenv("TAVILY_API_KEY"))

@tool
def tavily_search(query: str) -> str:
    """Uses the Tavily Search API to find information on the web."""
    print(f"--- Calling Tavily Search Tool with query: {query} ---")
    try:
        result = tavily.search(query=query, search_depth="advanced")
        return f"Search results for '{query}':\n" + "\n".join([f"- {r['content']}" for r in result['results']])
    except Exception as e: return f"Error during Tavily search: {e}"

@tool
def read_file(url: str) -> str:
    """Downloads and reads the content of a file (text or PDF) from a URL."""
    print(f"--- Calling Read File Tool with URL: {url} ---")
    try:
        filename = os.path.join(FILES_DIR, os.path.basename(url))
        response = requests.get(url)
        response.raise_for_status()
        with open(filename, 'wb') as f: f.write(response.content)
        if url.lower().endswith('.pdf'):
            try:
                pdf_reader = pypdf.PdfReader(filename)
                return f"Successfully read PDF file '{filename}'. Content:\n\n{''.join(p.extract_text() for p in pdf_reader.pages)}"
            except Exception as e: return f"Error reading PDF file: {e}"
        else:
            try:
                with open(filename, 'r', encoding='utf-8') as f: return f"Successfully read text file '{filename}'. Content:\n\n{f.read()}"
            except UnicodeDecodeError: return f"Successfully downloaded binary file '{filename}'. Cannot display content as text."
    except requests.exceptions.RequestException as e: return f"Error downloading or reading file: {e}"

@tool
def python_interpreter(code: str) -> str:
    """Executes Python code and returns its stdout."""
    print(f"--- Calling Python Interpreter Tool with code:\n{code} ---")
    output_buffer = io.StringIO()
    try:
        with redirect_stdout(output_buffer): exec(code, globals())
        return f"Code executed successfully. Output:\n{output_buffer.getvalue()}"
    except Exception as e: return f"Error executing Python code: {e}"

#
# ================================================================================================
#  ✅ 2. CONFIGURE AND BUILD THE AGENT (Manual, Stable Method)
# ================================================================================================
#
def build_agent_graph():
    """Builds the agent using the most fundamental LangChain components."""
    tools = [tavily_search, read_file, python_interpreter]

    # 1. Create the ChatCohere model instance
    llm = ChatCohere(model="command-r-plus", temperature=0, cohere_api_key=os.getenv("COHERE_API_KEY"))

    # 2. Bind the tools to the LLM. This lets the LLM know about the tools.
    llm_with_tools = llm.bind_tools(tools)

    # 3. Create the prompt template. This is the core instruction for the agent.
    prompt = ChatPromptTemplate.from_messages([
        ("system", AGENT_SYSTEM_PROMPT),
        ("user", "{input}"),
        ("placeholder", "{agent_scratchpad}"), # This is where tool results will be injected.
    ])

    # 4. Define the agent runnable. This is a chain that pipes components together.
    #    It formats the input, sends it to the LLM, and parses the output.
    agent = (
        {
            "input": lambda x: x["input"],
            "agent_scratchpad": lambda x: format_cohere_tools(x["intermediate_steps"]),
        }
        | prompt
        | llm_with_tools
        | CohereToolsAgentOutputParser()
    )

    # 5. Create the AgentExecutor to run the agent-tool loop.
    agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

    return agent_executor

#
# ================================================================================================
#  ✅ 3. AGENT CLASS AND EVALUATION LOGIC
# ================================================================================================
#
class GaiaAgent:
    def __init__(self):
        print("GaiaAgent initialized. Building agent with fundamental LangChain components...")
        self.agent_app = build_agent_graph()

    def __call__(self, question: str) -> str:
        print(f"\n{'='*60}\nAgent received question: {question[:100]}...\n{'='*60}")
        try:
            # The standard agent executor expects 'input'.
            response = self.agent_app.invoke({"input": question})
            final_answer = str(response.get("output", "")).strip()
            print(f"\n--- Agent finished. Final Answer: {final_answer} ---\n")
            return final_answer
        except Exception as e:
            print(f"An error occurred during agent execution: {e}")
            return f"AGENT_EXECUTION_ERROR: {e}"

# --- The rest of the file is unchanged ---
def run_and_submit_all( profile: gr.OAuthProfile | None):
    space_id = os.getenv("SPACE_ID")
    if not profile: return "Please Login to Hugging Face with the button.", None
    username = f"{profile.username}"
    print(f"User logged in: {username}")
    api_url = DEFAULT_API_URL
    questions_url = f"{api_url}/questions"
    submit_url = f"{api_url}/submit"
    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"

    try:
        response = requests.get(questions_url, timeout=15)
        response.raise_for_status()
        questions_data = response.json()
    except Exception as e: return f"An unexpected error occurred fetching questions: {e}", None

    results_log, answers_payload = [], []
    agent_instance = GaiaAgent()

    for item in questions_data:
        task_id, question_text = item.get("task_id"), item.get("question")
        if not task_id or question_text is None: continue
        try:
            submitted_answer = agent_instance(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: return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}

    try:
        response = requests.post(submit_url, json=submission_data, timeout=90)
        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.')}"
        )
        return final_status, pd.DataFrame(results_log)
    except Exception as e: return f"An unexpected error in submission: {e}", pd.DataFrame(results_log)

with gr.Blocks() as demo:
    gr.Markdown("# GAIA Agent Final Assessment (Direct Cohere Integration)")
    gr.Markdown(
        """
        **Instructor's Note:** This version uses a fundamental, manual agent construction. It is the most stable and recommended approach, avoiding any version-specific helper functions.
        1.  Ensure you have a **`COHERE_API_KEY`** and a **`TAVILY_API_KEY`** set in your Space secrets.
        2.  Ensure your `requirements.txt` includes `langchain-cohere` and `langchain`.
        """
    )
    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)
    demo.launch(debug=True, share=False, ssr_mode=False)