added gaia agent

README.md

@@ -4,7 +4,6 @@ emoji: 🕵🏻‍♂️
 colorFrom: indigo
 colorTo: indigo
 sdk: gradio
-sdk_version: 5.25.2
 app_file: app.py
 pinned: false
 hf_oauth: true
@@ -12,4 +11,14 @@ hf_oauth: true
 hf_oauth_expiration_minutes: 480
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+## ⚠️ Configuration Required
+
+To run this space, you need to add your Hugging Face token to the space secrets. This is required for the agent to work.
+
+1.  Create a Hugging Face token with `read` access [here](https://huggingface.co/settings/tokens).
+2.  Go to your Space's **Settings** page.
+3.  Under **Secrets**, add a new secret.
+    -   **Name:** `HF_TOKEN`
+    -   **Value:** Paste your token here.
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -1,75 +1,283 @@
 import os
 import gradio as gr
 import requests
-import inspect
 import pandas as pd
+import re
+import io
+import contextlib
+from huggingface_hub import InferenceClient
+from langchain_community.tools import DuckDuckGoSearchRun
+from PyPDF2 import PdfReader
+from docx import Document
+import json
 
-# (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+# A powerful, open-source model with function-calling capabilities
+MODEL_ID = "NousResearch/Hermes-2-Pro-Mistral-7B"
+# This prompt template is inspired by the ReAct framework and is tailored for tool use.
+PROMPT_TEMPLATE = """<|im_start|>system
+You are a helpful assistant designed to answer questions accurately. You have access to the following tools:
 
-# --- Basic Agent Definition ---
-# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
-class BasicAgent:
+{tools_description}
+
+To answer the question, you must follow this format, thinking step by step.
+
+Thought: Your reasoning and plan for the next step. You can also write down observations here.
+Action: The tool to use, in the format `tool_name(arg_name="value")`. The available tools are: {tool_names}.
+Observation: The result from the tool.
+... (this Thought/Action/Observation can repeat N times)
+
+When you have the final answer, respond with:
+Thought: I have now found the final answer.
+Final Answer: The final answer.
+
+Do not use a tool if you are not sure about the parameters. Do not make up file names.
+Question: {question}<|im_end|>
+<|im_start|>assistant
+{scratchpad}"""
+
+
+# --- Tool Definitions ---
+
+class WebSearchTool:
+    """A tool to search the web for information."""
     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
+        self.search = DuckDuckGoSearchRun()
+
+    def __call__(self, query: str):
+        """
+        Searches the web for the given query.
+        Args:
+            query (str): The search query.
+        Returns:
+            str: The search results.
+        """
+        print(f"--- Calling WebSearchTool with query: {query} ---")
+        try:
+            return self.search.run(query)
+        except Exception as e:
+            return f"Error during web search: {e}"
+
+    @property
+    def description(self):
+        return 'web_search(query: str) -> str - A tool to search the web for information. Use it to find up-to-date information or facts.'
+
+class PythonREPLTool:
+    """A tool to execute Python code."""
+    def __call__(self, code: str):
+        """
+        Executes Python code and returns the output.
+        Args:
+            code (str): The Python code to execute.
+        Returns:
+            str: The output of the executed code.
+        """
+        print(f"--- Calling PythonREPLTool with code: {code} ---")
+        if "os" in code or "sys" in code or "subprocess" in code:
+            return "Error: Use of os, sys, or subprocess is not allowed."
+            
+        local_vars = {}
+        string_io = io.StringIO()
+        try:
+            with contextlib.redirect_stdout(string_io):
+                exec(code, {}, local_vars)
+            output = string_io.getvalue()
+            if not output and local_vars:
+                 # If there was no print statement, return the value of the last variable
+                output = str(list(local_vars.values())[-1])
+            return output if output else "Code executed with no output."
+        except Exception as e:
+            return f"Error executing code: {e}"
+
+    @property
+    def description(self):
+        return 'python_repl(code: str) -> str - A Python REPL. Use it to perform calculations, data manipulation, etc. The result of the last line is returned.'
+
+class FileReaderTool:
+    """A tool to read the content of a file associated with a task."""
+    def __init__(self, api_url: str):
+        self.api_url = api_url
+
+    def __call__(self, task_id: str, file_name: str):
+        """
+        Reads the content of a file.
+        Args:
+            task_id (str): The ID of the task the file is associated with.
+            file_name (str): The name of the file to read. The LLM must infer this from the question.
+        Returns:
+            str: The content of the file.
+        """
+        print(f"--- Calling FileReaderTool for task_id: {task_id}, file_name: {file_name} ---")
+        file_url = f"{self.api_url}/files/{task_id}"
+        
+        try:
+            response = requests.get(file_url, timeout=20)
+            response.raise_for_status()
+            
+            content = ""
+            file_content = io.BytesIO(response.content)
+            
+            if file_name.endswith('.pdf'):
+                pdf = PdfReader(file_content)
+                for page in pdf.pages:
+                    content += page.extract_text() if page.extract_text() else ""
+            elif file_name.endswith('.docx'):
+                doc = Document(file_content)
+                for para in doc.paragraphs:
+                    content += para.text + '\n'
+            elif file_name.endswith('.csv'):
+                df = pd.read_csv(file_content)
+                content = df.to_string()
+            elif file_name.endswith('.json'):
+                data = json.load(file_content)
+                content = json.dumps(data, indent=2)
+            elif file_name.endswith('.txt'):
+                content = file_content.read().decode('utf-8')
+            else:
+                return f"Error: Unsupported file type for '{file_name}'. Supported types: .pdf, .docx, .csv, .json, .txt."
+
+            return content if content else "File is empty."
+        
+        except requests.exceptions.RequestException as e:
+            return f"Error downloading file: {e}"
+        except Exception as e:
+            return f"Error reading file '{file_name}': {e}"
+
+    @property
+    def description(self):
+        return 'file_reader(task_id: str, file_name: str) -> str - Reads the content of a file associated with the current task. Use the file name mentioned in the question.'
+
+
+# --- GAIA Agent Definition ---
+class GaiaAgent:
+    def __init__(self, hf_token: str, api_url: str, max_turns: int = 8):
+        print("GaiaAgent initializing...")
+        if not hf_token:
+            raise ValueError("Hugging Face token is required for the Inference API.")
+        
+        self.llm_client = InferenceClient(model=MODEL_ID, token=hf_token)
+        self.max_turns = max_turns
+        
+        # Initialize tools
+        self.tools = {
+            "web_search": WebSearchTool(),
+            "python_repl": PythonREPLTool(),
+            "file_reader": FileReaderTool(api_url=api_url),
+        }
+        self.tools_description = "\n".join([f"- `{tool.description}`" for tool in self.tools.values()])
+        self.tool_names = ", ".join(self.tools.keys())
+        print("GaiaAgent initialized successfully.")
+
+    def __call__(self, question: str, task_id: str) -> str:
+        print(f"\n--- Running agent on task {task_id} ---")
+        print(f"Question: {question[:100]}...")
+        
+        scratchpad = ""
+        
+        for turn in range(self.max_turns):
+            print(f"Turn {turn + 1}/{self.max_turns}")
+
+            # 1. Construct the prompt
+            prompt = PROMPT_TEMPLATE.format(
+                tools_description=self.tools_description,
+                tool_names=self.tool_names,
+                question=question,
+                scratchpad=scratchpad,
+            )
+
+            # 2. Call the LLM
+            try:
+                llm_output = self.llm_client.text_generation(
+                    prompt, max_new_tokens=1024, stop_sequences=["<|im_end|>", "Observation:"], temperature=0.1
+                ).strip()
+            except Exception as e:
+                print(f"LLM API call failed: {e}")
+                return f"Error: LLM call failed. {e}"
+
+            print(f"LLM Output:\n{llm_output}")
+            scratchpad += llm_output
+
+            # 3. Parse the output for Final Answer or Action
+            final_answer_match = re.search(r"Final Answer:\s*(.*)", scratchpad, re.DOTALL)
+            action_match = re.search(r"Action:\s*([a-zA-Z0-9_]+)\((.*)\)", llm_output)
+
+            if final_answer_match:
+                answer = final_answer_match.group(1).strip()
+                print(f"Final Answer Found: {answer}")
+                return answer
+            
+            elif action_match:
+                tool_name = action_match.group(1).strip()
+                tool_args_str = action_match.group(2).strip()
+                
+                if tool_name not in self.tools:
+                    observation = f"Error: Unknown tool '{tool_name}'. Available tools: {self.tool_names}"
+                else:
+                    try:
+                        # Safely parse arguments
+                        args_dict = eval(f"dict({tool_args_str})", {"__builtins__": None}, {})
+                        
+                        if tool_name == 'file_reader':
+                            args_dict['task_id'] = task_id
+                            
+                        tool = self.tools[tool_name]
+                        observation = tool(**args_dict)
+                    except Exception as e:
+                        observation = f"Error executing tool '{tool_name}': {e}"
+                
+                print(f"Observation: {str(observation)[:200]}...")
+                scratchpad += f"\nObservation: {str(observation)}\n"
+            else:
+                print("No valid action or final answer found in LLM output. Continuing thought process.")
+                scratchpad += "\nObservation: No valid action taken. Please either use a tool with the correct format `Action: tool_name(arg_name=\"value\")` or provide the final answer in the format `Final Answer: your_answer`."
+
+        print("Agent reached max turns.")
+        return "Agent stopped after reaching maximum turns."
+
+# --- Main Submission Logic ---
+
+def run_and_submit_all(profile: gr.OAuthProfile | None):
+    hf_token = os.getenv("HF_TOKEN")
+    if not hf_token:
+        return "Error: `HF_TOKEN` environment variable not set. Please add it to your Space secrets.", None
+
+    space_id = os.getenv("SPACE_ID")
+    if not space_id:
+        return "Error: `SPACE_ID` environment variable not found. Are you running in a Hugging Face Space?", None
+
+    if not profile:
+        return "Please Login to Hugging Face with the button to submit.", None
+    
+    username = 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"
 
-    # 1. Instantiate Agent ( modify this part to create your agent)
+    # 1. Instantiate Agent
     try:
-        agent = BasicAgent()
+        agent = GaiaAgent(hf_token=hf_token, api_url=api_url)
     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)
+    print(f"Code link: {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
+        return f"Error fetching questions: {e}", None
 
-    # 3. Run your Agent
+    # 3. Run Agent and Collect Answers
     results_log = []
     answers_payload = []
     print(f"Running agent on {len(questions_data)} questions...")
@@ -77,10 +285,9 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
         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)
+            submitted_answer = agent(question_text, task_id)
             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:
@@ -88,18 +295,14 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
              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 
+    # 4. Prepare and 5. Submit
     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}")
+    print(f"Submitting {len(answers_payload)} answers for user '{username}'...")
+    
     try:
-        response = requests.post(submit_url, json=submission_data, timeout=60)
+        response = requests.post(submit_url, json=submission_data, timeout=120)
         response.raise_for_status()
         result_data = response.json()
         final_status = (
@@ -109,61 +312,42 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
             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)
+        error_detail = "Network error or server responded with an error."
+        if e.response is not None:
+             error_detail = f"Server responded with status {e.response.status_code}. Response: {e.response.text[:500]}"
+        status_message = f"Submission Failed: {error_detail}"
         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 ---
+# --- Gradio Interface ---
 with gr.Blocks() as demo:
-    gr.Markdown("# Basic Agent Evaluation Runner")
+    gr.Markdown("# GAIA 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.
-
+        1.  **Add your HF Token**: Go to the 'Settings' tab of this Space and add a secret named `HF_TOKEN` with your Hugging Face read token.
+        2.  **Login**: Log in to your Hugging Face account using the button below. This is required for submission.
+        3.  **Run**: 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.
+        **Disclaimer:**
+        This process can take several minutes as the agent processes each question. Please be patient.
         """
     )
 
-    gr.LoginButton()
-
-    run_button = gr.Button("Run Evaluation & Submit All Answers")
+    with gr.Row():
+        gr.LoginButton()
+        run_button = gr.Button("Run Evaluation & Submit All Answers", variant="primary")
 
     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(
@@ -173,24 +357,17 @@ with gr.Blocks() as demo:
 
 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")
+    if not os.getenv("HF_TOKEN"):
+        print("⚠️  WARNING: `HF_TOKEN` secret not found. The agent will not be able to run.")
     else:
-        print("ℹ️  SPACE_HOST environment variable not found (running locally?).")
+        print("✅ `HF_TOKEN` secret found.")
 
-    if space_id_startup: # Print repo URLs if SPACE_ID is found
+    space_id_startup = os.getenv("SPACE_ID")
+    if space_id_startup:
         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("ℹ️  SPACE_ID environment variable not found (running locally?).")
+    
     print("-"*(60 + len(" App Starting ")) + "\n")
-
-    print("Launching Gradio Interface for Basic Agent Evaluation...")
-    demo.launch(debug=True, share=False)
+    print("Launching Gradio Interface for GAIA Agent Evaluation...")
+    demo.launch()

requirements.txt

@@ -1,2 +1,9 @@
 gradio
-requests
+requests
+huggingface-hub
+langchain-community
+duckduckgo-search
+pypdf2
+python-docx
+pandas
+openpyxl