Update agent.py

agent.py

@@ -1,110 +1,114 @@
-"""LangGraph Agent – retry 5s, 30s, 60s; senza Supabase"""
-
-import os, time
-from dotenv import load_dotenv
-from langgraph.graph import START, StateGraph, MessagesState
-from langgraph.prebuilt import ToolNode, tools_condition
-
-# LLM providers
-from langchain_google_genai import ChatGoogleGenerativeAI
-from langchain_groq import ChatGroq
-from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint
-
-# Tools & loaders
-from langchain_community.tools.tavily_search import TavilySearchResults
-from langchain_community.document_loaders import WikipediaLoader, ArxivLoader
-from langchain_core.messages import SystemMessage, HumanMessage
-from langchain_core.tools import tool
-
-load_dotenv()
-
-# --------------------------------------------------------------------------- #
-#  TOOLS                                                                      #
-# --------------------------------------------------------------------------- #
-@tool
-def multiply(a: int, b: int) -> int:
-    """Multiply two integers and return the product."""
-    return a * b
-
-@tool
-def add(a: int, b: int) -> int:
-    """Add two integers and return the sum."""
-    return a + b
-
-@tool
-def subtract(a: int, b: int) -> int:
-    """Subtract the second integer from the first and return the difference."""
-    return a - b
-
-@tool
-def divide(a: int, b: int) -> float:
-    """Divide a by b and return the quotient (error if b == 0)."""
-    if b == 0:
-        raise ValueError("Cannot divide by zero.")
-    return a / b
-
-@tool
-def modulus(a: int, b: int) -> int:
-    """Return the remainder of the division of a by b."""
-    return a % b
-
-@tool
-def wiki_search(query: str) -> str:
-    """Search Wikipedia (max 2 docs) and return formatted content."""
-    docs = WikipediaLoader(query=query, load_max_docs=2).load()
-    return "\n\n---\n\n".join(
-        f'<Document source="{d.metadata["source"]}" page="{d.metadata.get("page","")}"/>\n'
-        f"{d.page_content}\n</Document>"
-        for d in docs
-    )
-
-@tool
-def web_search(query: str) -> str:
-    """Perform a web search with Tavily (max 3 docs) and return formatted content."""
-    docs = TavilySearchResults(max_results=3).invoke(query=query)
-    return "\n\n---\n\n".join(
-        f'<Document source="{d.metadata["source"]}" page="{d.metadata.get("page","")}"/>\n'
-        f"{d.page_content}\n</Document>"
-        for d in docs
-    )
-
-@tool
-def arxiv_search(query: str) -> str:
-    """Search ArXiv (max 3 docs) and return first 1000 characters per paper."""
-    docs = ArxivLoader(query=query, load_max_docs=3).load()
-    return "\n\n---\n\n".join(
-        f'<Document source="{d.metadata["source"]}" page="{d.metadata.get("page","")}"/>\n'
-        f"{d.page_content[:1000]}\n</Document>"
-        for d in docs
-    )
-
-# --------------------------------------------------------------------------- #
-#  System prompt                                                              #
-# --------------------------------------------------------------------------- #
-with open("system_prompt.txt", "r", encoding="utf-8") as f:
-    system_prompt = f.read()
-sys_msg = SystemMessage(content=system_prompt)
-
-tools = [
-    multiply, add, subtract, divide, modulus,
-    wiki_search, web_search, arxiv_search,
-]
-
-# --------------------------------------------------------------------------- #
-#  Retry parameters                                                           #
-# --------------------------------------------------------------------------- #
-RETRY_DELAYS = [0, 5, 30, 60]        # secondi: tentativo 0, 1, 2, 3
-MAX_ATTEMPTS = len(RETRY_DELAYS)
-
-# --------------------------------------------------------------------------- #
-#  Build LangGraph                                                            #
-# --------------------------------------------------------------------------- #
-def build_graph(provider: str = "groq"):
-    """Return a LangGraph graph with explicit retry logic (5s, 30s, 60s)."""
-
-    # ----------- LLM selection -------------------------------------------- #
-    if provider == "google":
-        llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0)
-
-    elif provider == "groq":
-        ll
+""" Basic Agent Evaluation Runner – invia sempre tutte le risposte """
+
+import os
+import requests
+import gradio as gr
+import pandas as pd
+from langchain_core.messages import HumanMessage
+from agent import build_graph
+
+
+# --- Constants ------------------------------------------------------------ #
+DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+
+
+# --- Agent wrapper -------------------------------------------------------- #
+class BasicAgent:
+    """LangGraph agent ready for evaluation."""
+    def __init__(self):
+        print("BasicAgent initialized (provider=groq).")
+        self.graph = build_graph(provider="groq")
+
+    def __call__(self, question: str) -> str:
+        print(f"Agent received question (first 50 chars): {question[:50]}...")
+        msgs = [HumanMessage(content=question)]
+        result = self.graph.invoke({"messages": msgs})
+        answer = result["messages"][-1].content
+        # rimuove la parte "FINAL ANSWER: "
+        return answer[14:]
+
+
+# --- Main evaluation logic ------------------------------------------------ #
+def run_and_submit_all(profile: gr.OAuthProfile | None):
+    # 0. Check login
+    if not profile:
+        return "Please Login to Hugging Face with the button.", None
+    username = profile.username
+    print(f"User logged in: {username}")
+
+    # 1. Instantiate agent
+    try:
+        agent = BasicAgent()
+    except Exception as e:
+        return f"Error initializing agent: {e}", None
+
+    # 2. Fetch questions
+    try:
+        resp = requests.get(f"{DEFAULT_API_URL}/questions", timeout=15)
+        resp.raise_for_status()
+        questions_data = resp.json()
+        if not questions_data:
+            return "Fetched questions list is empty.", None
+    except Exception as e:
+        return f"Error fetching questions: {e}", None
+
+    # 3. Run agent and build payload
+    answers_payload = []
+    results_log = []
+
+    for item in questions_data:
+        task_id = item.get("task_id")
+        q_text = item.get("question")
+
+        submitted_answer = "errore"  # default in caso di failure
+        try:
+            submitted_answer = agent(q_text)
+        except Exception as e:
+            print(f"Error running agent on task {task_id}: {e}")
+
+        # in ogni caso inseriamo la risposta (successo o errore)
+        answers_payload.append(
+            {"task_id": task_id, "submitted_answer": submitted_answer}
+        )
+        results_log.append(
+            {
+                "Task ID": task_id,
+                "Question": q_text,
+                "Submitted Answer": submitted_answer,
+            }
+        )
+
+    # 4. Submit answers
+    submission = {
+        "username": username,
+        "agent_code": f"https://huggingface.co/spaces/{os.getenv('SPACE_ID', '')}/tree/main",
+        "answers": answers_payload,
+    }
+
+    try:
+        resp = requests.post(f"{DEFAULT_API_URL}/submit", json=submission, timeout=60)
+        resp.raise_for_status()
+        data = resp.json()
+        status_msg = (
+            f"Submission Successful!\nUser: {data.get('username')}\n"
+            f"Overall Score: {data.get('score', 'N/A')}% "
+            f"({data.get('correct_count', '?')}/{data.get('total_attempted', '?')} correct)\n"
+            f"Message: {data.get('message', 'No message received.')}"
+        )
+    except Exception as e:
+        status_msg = f"Submission Failed: {e}"
+
+    return status_msg, pd.DataFrame(results_log)
+
+
+# --- Gradio UI ------------------------------------------------------------ #
+with gr.Blocks() as demo:
+    gr.Markdown("# Basic Agent Evaluation Runner (retry & error-safe)")
+    gr.LoginButton()
+    run_btn = gr.Button("Run Evaluation & Submit All Answers")
+    status_box = gr.Textbox(lines=5, label="Run Status / Submission Result")
+    results_tbl = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
+    run_btn.click(fn=run_and_submit_all, outputs=[status_box, results_tbl])
+
+if __name__ == "__main__":
+    demo.launch(debug=True, share=False)