Update app.py

app.py

@@ -5,100 +5,46 @@ import inspect
 import pandas as pd
 
 # Additional packages
-from langchain import hub
-from langchain_google_genai import ChatGoogleGenerativeAI
+from smolagents import CodeAgent, InferenceClientModel, OpenAIModel
 
 # (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
 # Added constants
-GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
-SERPER_API_KEY = os.getenv("SERPER_API_KEY")
-
-# --- 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
-
-class FinalAgent:
-    def __init__(self):
-        """Initializes the agent with the Gemini 2.5 Flash API and necessary tools."""
-        print("Initializing FinalAgent with official LangChain prompt...")
-        self.api_url = DEFAULT_API_URL
-
-        if not GOOGLE_API_KEY or not SERPER_API_KEY:
-            raise ValueError("Fatal Error: Both GOOGLE_API_KEY and SERPER_API_KEY secrets must be set.")
-
-        self.llm = ChatGoogleGenerativeAI(model="gemini-2.5-flash", temperature=0, convert_system_message_to_human=True, google_api_key=GOOGLE_API_KEY)
-        print("Gemini 2.5 Flash client initialized successfully.")
-
-        self.tools = self._setup_tools()
-
-        # Pull the official, tested prompt from the LangChain hub, instead of creating a custom prompt.
-        # This prompt is guaranteed to have the correct variables and structure.
-        prompt = hub.pull("hwchase17/react")
-
-        agent = create_react_agent(llm=self.llm, tools=self.tools, prompt=prompt)
-        self.executor = AgentExecutor(agent=agent, tools=self.tools, verbose=True, handle_parsing_errors=True, max_iterations=12)
-        print("FinalAgent initialized successfully.")
-
-    def _setup_tools(self):
-        """Defines the tools available to the agent."""
-        search = GoogleSerperAPIWrapper(serper_api_key=SERPER_API_KEY)
-
-        def file_downloader_tool(task_id: str) -> str:
-            try:
-                all_questions_response = requests.get(f"{self.api_url}/questions", timeout=10)
-                all_questions_response.raise_for_status()
-                all_questions = all_questions_response.json()
-                file_name = next((q.get('file') for q in all_questions if q.get('task_id') == task_id), None)
-                if not file_name: return f"Error: No file associated with task_id '{task_id}'."
-                local_path = f"/tmp/{file_name}"
-                if not os.path.exists(local_path):
-                    response = requests.get(f"{self.api_url}/files/{task_id}", timeout=10)
-                    response.raise_for_status()
-                    with open(local_path, "wb") as f: f.write(response.content)
-                return f"File '{file_name}' is available at local path: {local_path}"
-            except Exception as e: return f"Error downloading file for task {task_id}: {str(e)}"
-
-        def python_interpreter_tool(code: str) -> str:
-            try:
-                exec_globals = {"pd": pd, "io": io}
-                local_vars = {}
-                exec(code, exec_globals, local_vars)
-                return str(local_vars.get('output', 'Code executed successfully with no output.'))
-            except Exception as e: return f"Error executing Python code: {str(e)}"
-
-        return [
-            Tool(name"Search", func=search.run, description="A useful tool for searching the internet to answer questions about current events, facts, and general knowledge."),
-            Tool(name="FileDownloader", func=file_downloader_tool, description="Use to download a file associated with a task. The input MUST be the task_id. Returns the local path where the file is stored."),
-            Tool(name="PythonInterpreter", func=python_interpreter_tool, description="Use to execute Python code for calculations or data analysis. Use this to analyze files AFTER donwloading them. You have the pandas library available as 'pd'. Example: df = pd.read_excel('/tmp/filename.xlsx')"),
-        ]
-
-    def __call__(self, item: dict) -> str:
-        """Runs the agent on a single question item."""
-        task_id = item.get("task_id")
-        question = item.get("question")
+GAIA_SYSTEM_PROMPT = """You are solving GAIA level 1 questions with extreme precision.
+
+
+CRITICAL RULES:
+1. Return ONLY the final answer - no explanations, no context, no preamble
+2. For numbers: just the number (no units unless explicitly requested)
+3. For strings: just the answer (no articles like "the" or "a")
+4. For lists: format as "item1, item2, item3 (no quotes, no brackets)
+
+STRATEGY:
+- Use web search liberally - search multiple times with different keywords
+- Visit actual webpages to get complete information
+- Cross-reference multiple sources
+- Think step-by-step but output only the final answer
+- If you find relevant info but not the complete answer, search again with more specifc terms
+
+NEVER output:
+- "FINAL ANSWER:"
+- "The answer is:"
+- Explanations or reasoning
+- "No information found" (keep searching!)
+
+Examples of correct outputs:
+Question: "How many studio albums?" -> Answer: "7"
+Question: "What is the capital?" -> Answer: "Paris"
+Question: "List the winners" → Answer: "John, Mary, Bob"
+"""
+
+
+
 
-        prompt_input = f"Question: {question}"
-        if item.get("file"):
-            prompt_input += f"\n(This question has an associated file names '{item.get('file')}'. To use it, you must first call the FileDownloader tool with the task_id: ‘{task_id}‘)"
 
-        input_data = {"input": prompt_input}
 
-        print(f"Question: {question}")
-        try:
-            response = self.executor.invoke(input_data)
-            return str(response.get("output", "Agent failed to produce an answer.")).strip()
-        except Exception as e:
-            return f"AGENT_ERROR: {str(e)}"
 
 
 # Submission logic Unchanged