test2

app.py

@@ -1,121 +1,29 @@
+from openai import OpenAI
 import os
-import requests
-import gradio as gr
-from dotenv import load_dotenv
 
-# Load API key dari file .env
-load_dotenv()
-OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
-
-# GAIA API Endpoint
-GAIA_API = "https://agents-course-unit4-scoring.hf.space"
-
-# Ambil semua pertanyaan
-def get_questions():
-    response = requests.get(f"{GAIA_API}/questions")
-    if response.status_code == 200:
-        return response.json()
-    else:
-        raise Exception("Gagal mengambil pertanyaan dari GAIA API")
-
-# Ambil file multimodal (jika ada)
-def get_file(task_id):
-    response = requests.get(f"{GAIA_API}/files/{task_id}")
-    if response.status_code == 200:
-        file_path = f"temp_{task_id}"
-        with open(file_path, "wb") as f:
-            f.write(response.content)
-        return file_path
-    else:
-        return None
-
-# Gunakan OpenAI atau model Hugging Face untuk menjawab
-def generate_answer(question_text, context=None):
-    prompt = f"""
-Jawablah pertanyaan berikut dengan jelas dan akurat:
-
-Pertanyaan:
-{question_text}
-
-Tambahan konteks:
-{context if context else "Tidak ada"}
-
-Jawaban:
-    """
-    # Jika pakai OpenAI
-    import openai
-    openai.api_key = OPENAI_API_KEY
-    response = openai.ChatCompletion.create(
-        model="gpt-4",
-        messages=[
-            {"role": "user", "content": prompt}
-        ],
-        temperature=0.7,
-        max_tokens=300
-    )
-    return response.choices[0].message.content.strip()
-
-# Fungsi utama: ambil pertanyaan dan jawab
-def answer_question(index):
-    try:
-        questions = get_questions()
-        if index < 0 or index >= len(questions):
-            return "Indeks pertanyaan tidak valid", "", ""
-
-        question = questions[index]
-        task_id = question["task_id"]
-        q_text = question["question"]
-
-        # Cek dan ambil file multimodal jika ada
-        file_path = None
-        if question.get("has_file"):
-            file_path = get_file(task_id)
-        
-        # Bisa dikembangkan: gunakan image/audio processing untuk context
-        context = f"File tersedia: {file_path}" if file_path else ""
-
-        # Generate jawaban
-        answer = generate_answer(q_text, context)
-
-        return q_text, context, answer
-
-    except Exception as e:
-        return f"Terjadi kesalahan: {str(e)}", "", ""
-
-# Untuk mengirim jawaban ke leaderboard (opsional)
-def submit_to_leaderboard(username, agent_code_url, task_id, answer):
-    data = {
-        "username": username,
-        "agent_code": agent_code_url,
-        "answers": [
-            {
-                "task_id": task_id,
-                "submitted_answer": answer
-            }
-        ]
-    }
-    response = requests.post(f"{GAIA_API}/submit", json=data)
-    if response.status_code == 200:
-        return f"Berhasil submit! Hasil: {response.json()}"
-    else:
-        return f"Gagal submit: {response.text}"
-
-# Gradio UI
-with gr.Blocks() as demo:
-    gr.Markdown("# 🤖 GAIA Question Answering Agent")
-    gr.Markdown("Masukkan indeks pertanyaan (0-19) untuk dijawab oleh agen AI.")
-    
-    index_input = gr.Number(label="Index Pertanyaan (0-19)", value=0, precision=0)
-    get_answer_btn = gr.Button("Dapatkan Jawaban")
-
-    q_output = gr.Textbox(label="Pertanyaan")
-    file_info = gr.Textbox(label="Info File (jika ada)")
-    answer_output = gr.Textbox(label="Jawaban Agen")
-
-    get_answer_btn.click(
-        fn=answer_question,
-        inputs=[index_input],
-        outputs=[q_output, file_info, answer_output]
-    )
-
-demo.launch()
+class BasicAgent:
+    def __init__(self):
+        api_key = os.getenv("OPENAI_API_KEY")
+        if not api_key:
+            raise ValueError("OPENAI_API_KEY environment variable not set.")
+        self.client = OpenAI(api_key=api_key)
+        print("BasicAgent (GPT-based) initialized.")
+
+    def __call__(self, question: str) -> str:
+        print(f"Agent received question (first 50 chars): {question[:50]}...")
+        try:
+            response = self.client.chat.completions.create(
+                model="gpt-4",  # Bisa diganti dengan "gpt-3.5-turbo" jika quota terbatas
+                messages=[
+                    {"role": "system", "content": "You are a helpful assistant."},
+                    {"role": "user", "content": question}
+                ],
+                temperature=0.7,
+                max_tokens=500,
+            )
+            answer = response.choices[0].message.content.strip()
+            print(f"Agent returning answer: {answer[:100]}...")
+            return answer
+        except Exception as e:
+            print(f"Error during OpenAI completion: {e}")
+            return f"[ERROR from agent: {str(e)}]"