Update app.py

app.py

@@ -1,196 +1,165 @@
+# app.py (Final version)
+
 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:
+import base64
+import json
+import operator
+from typing import Annotated, List, TypedDict
+
+from dotenv import load_dotenv
+from langchain_community.tools.tavily_search import TavilySearchResults
+from langchain_core.messages import BaseMessage, HumanMessage, AIMessage
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.tools import tool
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langgraph.graph import END, StateGraph
+from langgraph.prebuilt import ToolNode
+
+API_BASE_URL = "https://agents-course-unit4-scoring.hf.space"
+
+class GaiaLangGraphAgent:
     def __init__(self):
-        print("BasicAgent initialized.")
+        print("Initializing GaiaLangGraphAgent...")
+        load_dotenv()
+
+        class AgentState(TypedDict):
+            question: str
+            intermediate_steps: Annotated[List[BaseMessage], operator.add]
+        self.AgentState = AgentState
+
+        web_search_tool = TavilySearchResults(max_results=4)
+
+        @tool
+        def calculator(expression: str) -> str:
+            """Evaluates a simple mathematical expression."""
+            try:
+                import numexpr
+                return str(numexpr.evaluate(expression).item())
+            except Exception as e: return f"Error: {e}"
+
+        llm_vision = ChatGoogleGenerativeAI(model="gemini-1.5-pro-latest")
+
+        def get_file_path(file_name: str) -> str:
+            if not os.path.exists("task_files"): os.makedirs("task_files")
+            return os.path.join("task_files", file_name)
+
+        @tool
+        def file_reader(file_name: str) -> str:
+            """Reads a file, downloading if necessary. Handles text and images."""
+            local_path = get_file_path(file_name)
+            if not os.path.exists(local_path):
+                download_url = f"{API_BASE_URL}/files/{file_name}"
+                print(f"Downloading: {download_url}")
+                try:
+                    response = requests.get(download_url); response.raise_for_status()
+                    with open(local_path, "wb") as f: f.write(response.content)
+                except Exception as e: return f"Error downloading {file_name}: {e}"
+            try:
+                if any(file_name.lower().endswith(ext) for ext in ['.png', '.jpg', '.jpeg', '.webp']):
+                    with open(local_path, "rb") as image_file: b64_image = base64.b64encode(image_file.read()).decode('utf-8')
+                    vision_prompt = HumanMessage(content=[
+                        {"type": "text", "text": "Describe this image in detail, focusing on text or identifiable objects."},
+                        {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{b64_image}"}}
+                    ])
+                    return llm_vision.invoke([vision_prompt]).content
+                else:
+                    with open(local_path, 'r', encoding='utf-8') as f: return f.read()
+            except Exception as e: return f"Error processing {file_name}: {e}"
+
+        tools = [web_search_tool, file_reader, calculator]
+
+        llm = ChatGoogleGenerativeAI(model="gemini-1.5-flash-latest", temperature=0, convert_system_message_to_human=True)
+        llm_with_tools = llm.bind_tools(tools)
+
+        planner_prompt = ChatPromptTemplate.from_messages([
+            ("system", """You are a world-class AI assistant.
+            **Principles:** 1. Analyze the question for nuances. 2. Create multi-step plans. 3. Use tools intelligently (search, file read, calculator) or solve logic puzzles directly. 4. Provide exact-match answers.
+            **Execution:** Loop through plan->act cycles until you have the final answer."""),
+            ("human", "{question}\n\n{intermediate_steps}"),
+        ])
+
+        def planner_node(state: AgentState):
+            print("\n---PLANNER---")
+            chain = planner_prompt | llm_with_tools
+            response = chain.invoke(state)
+            print(f"Planner decision: {'Tool call' if response.tool_calls else 'Final Answer'}")
+            return {'intermediate_steps': [response]}
+
+        tool_node = ToolNode(tools)
+
+        def should_continue(state: AgentState):
+            last_message = state['intermediate_steps'][-1]
+            if isinstance(last_message, AIMessage):
+                if len(getattr(last_message, "tool_calls", [])) > 0: return "action"
+            return END
+
+        workflow = StateGraph(AgentState)
+        workflow.add_node("planner", planner_node)
+        workflow.add_node("action", tool_node)
+        workflow.set_entry_point("planner")
+        workflow.add_conditional_edges("planner", should_continue)
+        workflow.add_edge("action", "planner")
+        self.app = workflow.compile()
+        print("GaiaLangGraphAgent initialized successfully.")
+
     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)
+        print(f"\n>>>>>> AGENT EXECUTING FOR QUESTION: {question[:70]}...")
+        initial_state = {"question": question, "intermediate_steps": []}
+        final_state = self.app.invoke(initial_state, config={"recursion_limit": 15})
+        final_answer = final_state["intermediate_steps"][-1].content
+        print(f"<<<<<< AGENT FINISHED. FINAL ANSWER: {final_answer}")
+        return final_answer
+
+def run_and_submit_all(profile: gr.OAuthProfile | None):
+    if not profile: return "Please Login to Hugging Face with the button first.", None
+    space_id = os.getenv("SPACE_ID")
+    if not space_id: return "CRITICAL ERROR: SPACE_ID not found. Run this from a deployed Hugging Face Space.", None
+    username = profile.username
+    print(f"User logged in: {username}")
+    questions_url = f"{API_BASE_URL}/questions"
+    submit_url = f"{API_BASE_URL}/submit"
     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 = GaiaLangGraphAgent()
+    except Exception as e: return f"Error initializing agent: {e}", None
     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()
+        response = requests.get(questions_url, timeout=20); 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...")
+    except Exception as e: return f"Error fetching questions: {e}", None
+    results_log, answers_payload = [], []
+    print(f"Running agent on {len(questions_data)} questions. This may take several minutes...")
     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
+        task_id, question_text = item.get("task_id"), item.get("question")
         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}")
+    submission_data = {"username": username, "agent_code": agent_code, "answers": answers_payload}
+    print(f"Submitting {len(answers_payload)} answers...")
     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.')}"
-        )
-        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.
-        """
-    )
+        final_status = (f"Submission Successful!\nUser: {result_data.get('username')}\n"
+                        f"Score: {result_data.get('score', 'N/A')}% "
+                        f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)")
+        return final_status, pd.DataFrame(results_log)
+    except Exception as e: return f"Submission Failed: {e}", pd.DataFrame(results_log)
 
+with gr.Blocks() as demo:
+    gr.Markdown("# GAIA - Advanced Agent Runner")
+    gr.Markdown("Log in and click 'Run' to evaluate the agent.")
     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, 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)
+    print("Launching Gradio Interface...")
+    demo.launch()