Update app.py

app.py

@@ -4,21 +4,104 @@ import requests
 import inspect
 import pandas as pd
 
+# Additional packages
+from langchain import hub
+from langchain_google_genai import ChatGoogleGenerativeAI
+
 # (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:
+#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):
-        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
+        """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")
+
+        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
 def run_and_submit_all( profile: gr.OAuthProfile | None):
     """
     Fetches all questions, runs the BasicAgent on them, submits all answers,