....

app.py

@@ -1,196 +1,399 @@
 import os
 import gradio as gr
 import requests
-import inspect
 import pandas as pd
+import logging
+import time
+import json
+from typing import Optional, Dict, Any
+from functools import lru_cache
+
+# Attempt to import Google Gemini library
+try:
+    import google.generativeai as genai
+except ImportError:
+    genai = None
+    print("WARNING: google-generativeai library not found. Install with: pip install google-generativeai")
+
+# Attempt to import search libraries (DuckDuckGo, Google Search)
+try:
+    from duckduckgo_search import DDGS
+except ImportError:
+    DDGS = None
+    print("WARNING: duckduckgo_search library not found. Gaia agent's DDG search will be unavailable.")
+
+try:
+    from googleapiclient.discovery import build as build_google_search_service
+    from googleapiclient.errors import HttpError as GoogleAPIHttpError
+except ImportError:
+    build_google_search_service = None
+    GoogleAPIHttpError = None
+    print("WARNING: google-api-python-client not found. Gaia agent's Google Custom Search will be unavailable.")
+
 
-# (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):
+DEFAULT_API_URL = os.getenv("SCORING_API_URL", "https://agents-course-unit4-scoring.hf.space")
+
+# API Keys from Environment Variables
+GOOGLE_CUSTOM_SEARCH_API_KEY = os.getenv("GOOGLE_API_KEY") 
+GOOGLE_CUSTOM_SEARCH_CSE_ID = os.getenv("GOOGLE_CSE_ID") 
+GOOGLE_GEMINI_API_KEY = os.getenv("GOOGLE_GEMINI_API_KEY")
+
+AGENT_DEFAULT_TIMEOUT = 15  # seconds
+
+# --- GaiaLevel1Agent Definition ---
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+)
+gaia_logger = logging.getLogger('gaia_agent_llm')
+
+class GoogleRateLimitError(Exception):
+    """Custom exception for Google API rate limit errors."""
+    pass
+
+class GaiaLevel1Agent:
     """
-    Fetches all questions, runs the BasicAgent on them, submits all answers,
-    and displays the results.
+    GAIA Level 1 Agent that answers questions using file content, web search,
+    and a Google Gemini LLM for interpretation and answer generation.
     """
-    # --- Determine HF Space Runtime URL and Repo URL ---
-    space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code
+    def __init__(self, api_url: str = DEFAULT_API_URL):
+        self.api_url = api_url
+        self.search_providers: Dict[str, Any] = {}
+        self.llm_model: Optional[Any] = None # For Gemini model
 
-    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
+        # Initialize DuckDuckGo search
+        if DDGS:
+            try:
+                self.search_providers['duckduckgo'] = DDGS()
+                gaia_logger.info("DuckDuckGo search initialized for GaiaAgent")
+            except Exception as e:
+                gaia_logger.error(f"Error initializing DuckDuckGo search for GaiaAgent: {e}")
+        
+        # Initialize Google Custom Search
+        if build_google_search_service and GOOGLE_CUSTOM_SEARCH_API_KEY and GOOGLE_CUSTOM_SEARCH_CSE_ID:
+            try:
+                self.search_providers['google_custom_search'] = build_google_search_service(
+                    "customsearch", "v1", developerKey=GOOGLE_CUSTOM_SEARCH_API_KEY
+                )
+                gaia_logger.info("Google Custom Search service initialized for GaiaAgent")
+            except Exception as e:
+                gaia_logger.error(f"Error initializing Google Custom Search service for GaiaAgent: {e}")
+        elif build_google_search_service:
+            gaia_logger.warning("Google Custom Search API Key or CSE ID not found. Google Custom Search for GaiaAgent will be unavailable.")
+        
+        # Initialize Google Gemini LLM
+        if genai and GOOGLE_GEMINI_API_KEY:
+            try:
+                genai.configure(api_key=GOOGLE_GEMINI_API_KEY)
+                self.llm_model = genai.GenerativeModel('gemini-pro')
+                gaia_logger.info("Google Gemini LLM ('gemini-pro') initialized successfully.")
+            except Exception as e:
+                gaia_logger.error(f"Error initializing Google Gemini LLM: {e}")
+                self.llm_model = None
+        else:
+            if not genai:
+                gaia_logger.warning("Google Gemini library (google-generativeai) not installed.")
+            if not GOOGLE_GEMINI_API_KEY:
+                gaia_logger.warning("GOOGLE_GEMINI_API_KEY not found. Google Gemini LLM will be unavailable.")
+            self.llm_model = None
 
-    api_url = DEFAULT_API_URL
-    questions_url = f"{api_url}/questions"
-    submit_url = f"{api_url}/submit"
+        if not self.search_providers:
+            gaia_logger.warning("No web search providers available for GaiaAgent.")
+        if not self.llm_model:
+            gaia_logger.warning("LLM model (Gemini) is not available. Agent will have limited capabilities.")
+            
+        gaia_logger.info(f"GaiaLevel1Agent initialized with scoring API: {self.api_url}")
+
+    @lru_cache(maxsize=32)
+    def _fetch_file_content(self, task_id: str) -> Optional[str]:
+        # (Same as before, logging prefixes might change if desired)
+        file_url = f"{self.api_url}/files/{task_id}"
+        gaia_logger.info(f"GaiaAgent fetching file from: {file_url}")
+        for attempt in range(3):
+            try:
+                response = requests.get(file_url, timeout=AGENT_DEFAULT_TIMEOUT)
+                response.raise_for_status()
+                content_type = response.headers.get("Content-Type", "")
+                if "text" in content_type or "json" in content_type or "csv" in content_type:
+                    return response.text
+                return None
+            except requests.exceptions.HTTPError as e:
+                if e.response.status_code == 404: return None
+                gaia_logger.warning(f"GaiaAgent: HTTP error fetching file: {e}")
+            except requests.exceptions.Timeout:
+                if attempt < 2: time.sleep(1)
+            except Exception as e:
+                if attempt < 2: time.sleep(1)
+        return None
+
+    def _perform_duckduckgo_search(self, query: str, max_results: int = 3) -> Optional[str]:
+        # (Same as before)
+        if 'duckduckgo' not in self.search_providers: return None
+        gaia_logger.info(f"GaiaAgent performing DuckDuckGo search for: '{query}'")
+        try:
+            results = self.search_providers['duckduckgo'].text(query, max_results=max_results)
+            if not results: return None
+            return "\n\n".join([f"DDG Result: {r.get('title', '')}\n{r.get('body', '')}" for r in results])
+        except Exception as e: gaia_logger.error(f"GaiaAgent: DuckDuckGo search error: {e}"); return None
+
+    def _perform_google_custom_search(self, query: str, num_results: int = 3) -> Optional[str]:
+        # (Modified to use renamed constants, otherwise same logic)
+        if 'google_custom_search' not in self.search_providers or not GoogleAPIHttpError: return None
+        gaia_logger.info(f"GaiaAgent performing Google Custom Search for: '{query}'")
+        try:
+            google_service: Any = self.search_providers['google_custom_search']
+            result: Any = google_service.cse().list(q=query, cx=GOOGLE_CUSTOM_SEARCH_CSE_ID, num=num_results).execute()
+            items = result.get('items', [])
+            if not items: gaia_logger.info("GaiaAgent: Google Custom Search returned no results."); return None
+            return "\n\n".join([f"Google Result: {item.get('title', '')}\n{item.get('snippet', '')}" for item in items])
+        except GoogleAPIHttpError as e:
+            # (Rate limit detection logic remains the same)
+            is_rate_limit = False; api_error_reason = "Unknown"
+            if e.resp.status == 429 or e.resp.status == 403:
+                try:
+                    error_details = json.loads(e.content.decode('utf-8'))
+                    if isinstance(error_details.get('error'), dict):
+                        errors_list = error_details['error'].get('errors', [])
+                        if errors_list and isinstance(errors_list[0], dict): api_error_reason = errors_list[0].get('reason', 'UnknownReason')
+                        message = error_details['error'].get('message', '').lower()
+                        if "rate limit" in message or "quota exceeded" in message or "daily limit exceeded" in message:
+                            if api_error_reason == 'UnknownReason' or api_error_reason == 'forbidden' or api_error_reason == 'backendError': api_error_reason = "rateLimitSuspectedInMessage"
+                    rate_limit_reasons = ['rateLimitExceeded', 'userRateLimitExceeded', 'quotaExceeded', 'dailyLimitExceeded', 'rateLimitSuspectedInMessage']
+                    if api_error_reason in rate_limit_reasons: is_rate_limit = True
+                except Exception: pass # Ignore parsing errors for simplicity here
+            if is_rate_limit:
+                log_msg = f"GaiaAgent: Google Custom Search API rate limit identified. Reason: {api_error_reason}."
+                if api_error_reason in ['quotaExceeded', 'dailyLimitExceeded']: log_msg += " (Daily free tier quota might be exceeded)."
+                gaia_logger.warning(log_msg)
+                raise GoogleRateLimitError(f"Google API rate limit: {api_error_reason}") from e
+            gaia_logger.error(f"GaiaAgent: Google Custom Search API HttpError: {e}"); return None
+        except Exception as e: gaia_logger.error(f"GaiaAgent: Unexpected error during Google Custom Search: {e}", exc_info=True); return None
+
+    def _formulate_answer_with_llm(self, question: str, file_context: Optional[str], web_context: Optional[str]) -> str:
+        if not self.llm_model:
+            gaia_logger.warning("LLM model (Gemini) not available for answer formulation.")
+            # Fallback to a simple rule if LLM is not there
+            if web_context: return f"Based on web search: {web_context.splitlines()[0] if web_context.splitlines() else 'No specific snippet found.'}"
+            if file_context: return f"Based on the provided document (first 100 chars): {file_context[:100]}..."
+            return "I am currently unable to process this request fully as the LLM is not available. I don't have enough information without it."
+
+        prompt_parts = [
+            "You are a helpful AI assistant. Your task is to answer the user's question based *only* on the provided context from a document and/or web search results. Be concise and directly answer the question.",
+            "If the provided context is insufficient to answer the question, clearly state that the information is not available in the provided materials.",
+            "\nUser Question: ",
+            question
+        ]
+
+        if file_context:
+            prompt_parts.append("\n\nContext from Provided Document:\n---")
+            prompt_parts.append(file_context[:4000]) # Limit context size for the prompt
+            prompt_parts.append("---")
+        else:
+            prompt_parts.append("\n\nContext from Provided Document:\n---")
+            prompt_parts.append("No document was provided or it could not be retrieved.")
+            prompt_parts.append("---")
+
+
+        if web_context:
+            prompt_parts.append("\n\nContext from Web Search Results:\n---")
+            prompt_parts.append(web_context[:4000]) # Limit context size
+            prompt_parts.append("---")
+        else:
+            prompt_parts.append("\n\nContext from Web Search Results:\n---")
+            prompt_parts.append("No web search was performed or no relevant results were found.")
+            prompt_parts.append("---")
+        
+        prompt_parts.append("\n\nAnswer:")
+        final_prompt = "\n".join(prompt_parts)
+        
+        gaia_logger.info(f"GaiaAgent sending prompt to Gemini (first 300 chars): {final_prompt[:300]}...")
+
+        try:
+            # More robust generation config
+            generation_config = genai.types.GenerationConfig(
+                # candidate_count=1, # default
+                # stop_sequences=["\n\n"], # Optional: if you want to force shorter answers
+                max_output_tokens=1024, # Or a number appropriate for your expected answer length
+                temperature=0.7, # Adjust for creativity vs. factuality
+                # top_p=0.9, # Adjust as needed
+                # top_k=40   # Adjust as needed
+            )
+            # Safety settings (example - adjust as needed)
+            safety_settings = [
+                {"category": "HARM_CATEGORY_HARASSMENT", "threshold": "BLOCK_MEDIUM_AND_ABOVE"},
+                {"category": "HARM_CATEGORY_HATE_SPEECH", "threshold": "BLOCK_MEDIUM_AND_ABOVE"},
+                {"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT", "threshold": "BLOCK_MEDIUM_AND_ABOVE"},
+                {"category": "HARM_CATEGORY_DANGEROUS_CONTENT", "threshold": "BLOCK_MEDIUM_AND_ABOVE"},
+            ]
+
+            response = self.llm_model.generate_content(
+                final_prompt,
+                generation_config=generation_config,
+                safety_settings=safety_settings
+            )
+            
+            # Check for blocked response due to safety
+            if not response.candidates or response.prompt_feedback.block_reason:
+                block_reason_str = response.prompt_feedback.block_reason.name if response.prompt_feedback.block_reason else "Unknown"
+                safety_ratings_str = ", ".join([f"{rating.category.name}: {rating.probability.name}" for rating in response.prompt_feedback.safety_ratings])
+                gaia_logger.warning(f"Gemini response blocked. Reason: {block_reason_str}. Safety ratings: [{safety_ratings_str}]")
+                return f"My response was blocked due to safety concerns (Reason: {block_reason_str}). Please rephrase your question or check the context."
+
+            llm_answer = response.text
+            gaia_logger.info(f"GaiaAgent received answer from Gemini (first 100 chars): {llm_answer[:100]}...")
+            return llm_answer
+        except Exception as e:
+            gaia_logger.error(f"Error calling Google Gemini API: {e}", exc_info=True)
+            return "I encountered an error while trying to generate an answer using the LLM. Please try again."
+
+
+    def __call__(self, question: str, task_id: Optional[str] = None) -> str:
+        gaia_logger.info(f"GaiaAgent processing question: '{question[:100]}...', Task ID: {task_id}")
+
+        # Simple identity check - can be handled without LLM
+        question_lower = question.lower().strip()
+        if "what is your name" in question_lower or "who are you" in question_lower:
+            return "I am a GAIA-oriented AI assistant. I use web search and document content, processed by an LLM, to answer questions."
+
+        context_from_file = ""
+        if task_id and any(kw in question.lower() for kw in ["document", "file", "provided text", "attached", "read", "content"]):
+            file_content = self._fetch_file_content(task_id)
+            if file_content:
+                context_from_file = file_content
+                gaia_logger.info(f"GaiaAgent: File content fetched ({len(context_from_file)} chars)")
+        
+        context_from_web = ""
+        needs_web_search = False
+        web_search_keywords = ["what is", "who is", "how to", "when did", "where is", "current", 
+                              "latest", "news", "define", "capital", "president", "recent", "explain"]
+        if any(kw in question.lower() for kw in web_search_keywords):
+            needs_web_search = True
+        if not context_from_file and len(question.split()) > 4: # If no file and question is not trivial
+             needs_web_search = True
+        
+        if needs_web_search:
+            search_query = question.replace("?", "").strip()
+            web_search_results: Optional[str] = None
+            if 'google_custom_search' in self.search_providers:
+                try:
+                    gaia_logger.info("GaiaAgent: Attempting Google Custom Search.")
+                    web_search_results = self._perform_google_custom_search(search_query)
+                except GoogleRateLimitError:
+                    gaia_logger.warning("GaiaAgent: Google Custom Search rate limit hit. Falling back to DuckDuckGo.")
+                    if 'duckduckgo' in self.search_providers:
+                        web_search_results = self._perform_duckduckgo_search(search_query)
+                except Exception as e: 
+                    gaia_logger.error(f"GaiaAgent: Error during Google Custom Search attempt: {e}")
+            
+            if not web_search_results and 'duckduckgo' in self.search_providers: # Fallback or primary if Google CS not used/failed
+                gaia_logger.info("GaiaAgent: Attempting DuckDuckGo Search.")
+                web_search_results = self._perform_duckduckgo_search(search_query)
+
+            if web_search_results:
+                context_from_web = web_search_results
+                gaia_logger.info(f"GaiaAgent: Web search results obtained ({len(context_from_web)} chars)")
+            else:
+                gaia_logger.warning("GaiaAgent: Web search did not yield results.")
+        
+        # Use LLM to formulate the final answer
+        answer = self._formulate_answer_with_llm(question, context_from_file, context_from_web)
+        gaia_logger.info(f"GaiaAgent final answer (first 100 chars): {answer[:100]}...")
+        return answer
+
+# --- Evaluation Runner (largely unchanged, agent instantiation and call are key) ---
+def run_and_submit_all(profile: gr.OAuthProfile | None):
+    space_id = os.getenv("SPACE_ID")
+    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
+
+    questions_url = f"{DEFAULT_API_URL}/questions"
+    submit_url = f"{DEFAULT_API_URL}/submit"
 
-    # 1. Instantiate Agent ( modify this part to create your agent)
     try:
-        agent = BasicAgent()
+        agent = GaiaLevel1Agent(api_url=DEFAULT_API_URL) # Instantiates our LLM agent
+        print("GaiaLevel1Agent (LLM-enabled) initialized for evaluation.")
     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}")
+        print(f"Error instantiating agent: {e}"); return f"Error initializing agent: {e}", None
+    
+    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" if space_id else "Code link not available"
+    print(f"Agent code link: {agent_code}")
+
     try:
-        response = requests.get(questions_url, timeout=15)
-        response.raise_for_status()
+        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
+        if not questions_data or not isinstance(questions_data, list):
+             print(f"Fetched questions list is empty/invalid: {questions_data}"); return "Fetched questions list is empty/invalid.", 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
+    except Exception as e: print(f"Error fetching questions: {e}"); return f"Error fetching questions: {e}", None
 
-    # 3. Run your Agent
-    results_log = []
-    answers_payload = []
+    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")
+        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}")
+            print(f"Skipping item with missing data: {item}")
+            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": "SKIPPED - Missing data"})
             continue
         try:
-            submitted_answer = agent(question_text)
+            submitted_answer = agent(question=question_text, task_id=task_id) # Agent call
             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}")
+             gaia_logger.error(f"Exception in agent call for task {task_id}", exc_info=True)
              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)
+        if not results_log: results_log.append({"Task ID": "N/A", "Question": "N/A", "Submitted Answer": "No questions processed."})
+        return "Agent did not produce any answers.", 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}")
+    print(f"Submitting {len(answers_payload)} answers for user '{username}'...")
     try:
-        response = requests.post(submit_url, json=submission_data, timeout=60)
-        response.raise_for_status()
+        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.')}"
-        )
+        final_status = (f"Submission Successful!\nUser: {result_data.get('username')}\nOverall 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.')}")
         print("Submission successful.")
-        results_df = pd.DataFrame(results_log)
-        return final_status, results_df
+        return final_status, pd.DataFrame(results_log)
     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
+        error_detail = f"Server responded: {e.response.status_code}."
+        try: error_detail += f" Detail: {e.response.json().get('detail', e.response.text)}"
+        except Exception: error_detail += f" Response: {e.response.text[:200]}"
+        print(f"Submission Failed: {error_detail}")
+        return f"Submission Failed: {error_detail}", pd.DataFrame(results_log)
     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
-
+        print(f"Submission Failed: {e}"); return f"Submission Failed: {e}", pd.DataFrame(results_log)
 
-# --- Build Gradio Interface using Blocks ---
+# --- Gradio Interface (Unchanged) ---
 with gr.Blocks() as demo:
-    gr.Markdown("# Basic Agent Evaluation Runner")
+    gr.Markdown("# Gaia Level 1 Agent (LLM-Powered) 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.  This space runs the Gaia Level 1 Agent, now powered by Google Gemini for answer generation.
+        2.  Log in to your Hugging Face account using the button below.
+        3.  Click 'Run Evaluation & Submit All Answers' to process questions 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.
+        **Note:** Ensure `GOOGLE_GEMINI_API_KEY`, `GOOGLE_API_KEY` (for Custom Search), and `GOOGLE_CSE_ID` are set as secrets in your Space for full functionality.
         """
     )
-
     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]
-    )
+    run_button.click(fn=run_and_submit_all, inputs=[gr.State()], 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.")
-
+    # Startup checks for API keys
+    if not GOOGLE_GEMINI_API_KEY: print("⚠️ WARNING: GOOGLE_GEMINI_API_KEY not set. LLM functionality will be disabled.")
+    else: print("✅ GOOGLE_GEMINI_API_KEY found.")
+    if not GOOGLE_CUSTOM_SEARCH_API_KEY or not GOOGLE_CUSTOM_SEARCH_CSE_ID: print("⚠️ WARNING: GOOGLE_API_KEY (for Custom Search) or GOOGLE_CSE_ID not set. Google Custom Search will be unavailable.")
+    else: print("✅ Google Custom Search API Key and CSE ID found.")
+    
     print("-"*(60 + len(" App Starting ")) + "\n")
-
-    print("Launching Gradio Interface for Basic Agent Evaluation...")
+    print("Launching Gradio Interface for Gaia Level 1 Agent (LLM-Powered) Evaluation...")
     demo.launch(debug=True, share=False)

requirements.txt

@@ -1,2 +1,7 @@
 gradio
-requests
+requests
+duckduckgo-search
+google-api-python-client
+httpx[http2]
+google-generativeai
+pandas