Update agent.py

agent.py

@@ -1,90 +1,98 @@
-# app.py – przywrócony layout benchmarku z poprawionym wywołaniem agenta
+# agent.py — final version without circular imports
 
 import os
-import requests
-import pandas as pd
-import gradio as gr
 import asyncio
+from llama_index.llms.openai import OpenAI
+from llama_index.core.agent.react.base import ReActAgent
+from llama_index.core.tools import FunctionTool
 
-from agent import answer_question
+from langchain_community.tools.wikipedia.tool import WikipediaQueryRun
+from langchain_community.utilities.wikipedia import WikipediaAPIWrapper
+from langchain_experimental.tools.python.tool import PythonREPLTool
+from langchain_community.document_loaders import YoutubeLoader
 
-DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+import whisper
+import openpyxl
 
-class GAIALlamaAgent:
-    def __call__(self, question: str) -> str:
-        return asyncio.run(answer_question(question))
+# Check OpenAI key
+if os.getenv("OPENAI_API_KEY"):
+    print("✅ Detected OPENAI_API_KEY")
+else:
+    print("⚠️ Missing OPENAI_API_KEY – LLM may fail")
 
-def run_and_submit_all(profile: gr.OAuthProfile | None):
-    space_id = os.getenv("SPACE_ID")
-    if not profile or not profile.username:
-        return "Please Login to Hugging Face with the button.", None
+# Tools definitions
+api_wrapper = WikipediaAPIWrapper(top_k_results=1, doc_content_chars_max=1000)
 
-    username = profile.username.strip()
-    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" if space_id else ""
-    questions_url = f"{DEFAULT_API_URL}/questions"
-    submit_url = f"{DEFAULT_API_URL}/submit"
+def wikipedia_search(query: str) -> str:
+    return WikipediaQueryRun(api_wrapper=api_wrapper).run({"query": query})
 
+def run_python_with_output(code: str) -> str:
+    if "print(" not in code:
+        code = f"print({code})"
+    return PythonREPLTool().run(code)
+
+def get_youtube_transcript(url: str) -> str:
     try:
-        response = requests.get(questions_url, timeout=15)
-        response.raise_for_status()
-        questions_data = response.json()
+        loader = YoutubeLoader.from_youtube_url(url, add_video_info=False)
+        docs = loader.load()
+        return " ".join(d.page_content for d in docs)
     except Exception as e:
-        return f"Error fetching questions: {e}", None
-
-    agent = GAIALlamaAgent()
-    results_log = []
-    answers_payload = []
-
-    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:
-            continue
-        try:
-            submitted_answer = agent(question_text)
-        except Exception as e:
-            submitted_answer = f"[ERROR] {e}"
-        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})
-
-    submission_data = {"username": username, "agent_code": agent_code, "answers": answers_payload}
+        return "[YOUTUBE ERROR] " + str(e)
 
+def transcribe_audio(file_path: str) -> str:
     try:
-        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.')}"
-        )
-        return final_status, pd.DataFrame(results_log)
+        model = whisper.load_model("base")
+        res = model.transcribe(file_path)
+        return res["text"]
     except Exception as e:
-        return f"Submission Failed: {e}", pd.DataFrame(results_log)
-
-# --- Gradio Interface matching original benchmark ---
-with gr.Blocks() as demo:
-    gr.Markdown("# Basic Agent Evaluation Runner")
-    gr.Markdown("""
-    **Instructions:**
-    1. Please clone this space and modify the agent logic.
-    2. Log in to Hugging Face with the button.
-    3. Click 'Run Evaluation & Submit All Answers' to run the full GAIA test.
-    """)
-
-    gr.LoginButton()
+        return "[AUDIO ERROR] " + str(e)
 
-    run_button = gr.Button("Run Evaluation & Submit All Answers")
-    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
-    results_table = gr.DataFrame(label="Questions and Agent Answers")
-
-    run_button.click(fn=run_and_submit_all, outputs=[status_output, results_table])
+def extract_excel_total_food_sales(file_path: str) -> str:
+    try:
+        wb = openpyxl.load_workbook(file_path)
+        sheet = wb.active
+        total = 0.0
+        for _, category, amount in sheet.iter_rows(min_row=2, values_only=True):
+            if isinstance(category, str) and "food" in category.lower():
+                total += float(amount or 0)
+        return f"${total:.2f}"
+    except Exception as e:
+        return "[EXCEL ERROR] " + str(e)
+
+# Assemble tools
+TOOLS = [
+    FunctionTool.from_defaults(wikipedia_search),
+    FunctionTool.from_defaults(run_python_with_output),
+    FunctionTool.from_defaults(get_youtube_transcript),
+    FunctionTool.from_defaults(transcribe_audio),
+    FunctionTool.from_defaults(extract_excel_total_food_sales),
+]
+
+# LLM and Agent
+llm = OpenAI(model="gpt-4")
+agent = ReActAgent.from_tools(
+    tools=TOOLS,
+    llm=llm,
+    verbose=True,
+    system_prompt="""
+You are an expert AI assistant on the GAIA benchmark.
+
+Use available tools (Wikipedia, Python, YouTube transcript, audio, Excel).
+Output ONLY the final answer. No reasoning or commentary.
+Format exactly as requested (list, number, name, chess move, currency).
+If tool fails, output "Tool not available".
+""",
+)
+
+def answer_question_sync(question: str) -> str:
+    try:
+        resp = agent.chat(question)
+        if hasattr(resp, "response") and hasattr(resp.response, "content"):
+            return resp.response.content.strip()
+        return str(resp).strip()
+    except Exception as e:
+        print("❌ Agent exception:", e)
+        return "[ERROR] " + str(e)
 
-if __name__ == "__main__":
-    print("\n===== Application Startup =====")
-    space_id = os.getenv("SPACE_ID")
-    if space_id:
-        print(f"🔗 Space: https://huggingface.co/spaces/{space_id}")
-    demo.launch(debug=True)
+async def answer_question(question: str) -> str:
+    return answer_question_sync(question)