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Runtime error
Update app.py
Browse files
app.py
CHANGED
@@ -7,7 +7,7 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
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# --- Constants ---
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DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
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# ---
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class BasicAgent:
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def __init__(self):
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print("BasicAgent initialized.")
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@@ -19,38 +19,33 @@ class BasicAgent:
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)
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def __call__(self, question: str) -> str:
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inputs = self.tokenizer(
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outputs = self.llm.generate(**inputs)
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final =
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return f"FINAL ANSWER: {final}" if final else "FINAL ANSWER: UNKNOWN"
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# ---
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def run_and_submit_all(
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space_id = os.getenv("SPACE_ID", "your-username/your-space") # fallback
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if not username
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return "
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agent = BasicAgent()
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questions_url = f"{DEFAULT_API_URL}/questions"
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submit_url = f"{DEFAULT_API_URL}/submit"
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agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
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try:
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response.raise_for_status()
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questions_data = response.json()
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except Exception as e:
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return f"
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answers = []
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log = []
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for
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task_id =
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question =
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if not task_id or not question:
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continue
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try:
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@@ -61,40 +56,39 @@ def run_and_submit_all(username):
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log.append({"Task ID": task_id, "Question": question, "Submitted Answer": f"ERROR: {e}"})
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if not answers:
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return "No answers
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"username": username.strip(),
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"agent_code":
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"answers": answers
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}
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try:
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f"Submission Successful!\n"
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f"User: {
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f"Score: {
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f"({
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f"Message: {
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)
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return status, pd.DataFrame(log)
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except Exception as e:
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return f"Submission failed: {e}", pd.DataFrame(log)
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# --- Gradio UI ---
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with gr.Blocks() as demo:
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gr.Markdown("
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gr.Markdown("
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run_button = gr.Button("Run and Submit")
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status_output = gr.Textbox(label="Submission Status", lines=4
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results_table = gr.DataFrame(label="Submitted
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run_button.click(fn=run_and_submit_all, inputs=[
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if __name__ == "__main__":
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demo.launch(
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# --- Constants ---
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DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
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# --- BasicAgent Class ---
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class BasicAgent:
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def __init__(self):
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print("BasicAgent initialized.")
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)
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def __call__(self, question: str) -> str:
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prompt = f"{self.agent_prompt}\n\nQuestion: {question}"
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inputs = self.tokenizer(prompt, return_tensors="pt")
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outputs = self.llm.generate(**inputs, max_new_tokens=50)
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result = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
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final = result.split("FINAL ANSWER:")[-1].strip()
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return f"FINAL ANSWER: {final}" if final else "FINAL ANSWER: UNKNOWN"
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# --- Run and Submit Function ---
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def run_and_submit_all(profile):
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space_id = os.getenv("SPACE_ID", "your-username/your-space") # fallback
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if not profile or not getattr(profile, "username", None):
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return "❌ Please log in to Hugging Face first.", None
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username = profile.username
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agent = BasicAgent()
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try:
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questions = requests.get(f"{DEFAULT_API_URL}/questions", timeout=15).json()
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except Exception as e:
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return f"❌ Error fetching questions: {e}", None
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answers = []
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log = []
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for q in questions:
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task_id = q.get("task_id")
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question = q.get("question")
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if not task_id or not question:
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continue
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try:
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log.append({"Task ID": task_id, "Question": question, "Submitted Answer": f"ERROR: {e}"})
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if not answers:
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return "⚠️ No answers were generated.", pd.DataFrame(log)
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submission = {
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"username": username.strip(),
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"agent_code": f"https://huggingface.co/spaces/{space_id}/tree/main",
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"answers": answers
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}
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try:
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r = requests.post(f"{DEFAULT_API_URL}/submit", json=submission, timeout=30)
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r.raise_for_status()
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res = r.json()
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return (
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f"✅ Submission Successful!\n"
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f"User: {res.get('username')}\n"
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f"Score: {res.get('score', 'N/A')}% "
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f"({res.get('correct_count', '?')}/{res.get('total_attempted', '?')})\n"
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f"Message: {res.get('message', '')}"
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), pd.DataFrame(log)
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except Exception as e:
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return f"❌ Submission failed: {e}", pd.DataFrame(log)
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# --- Gradio UI ---
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with gr.Blocks() as demo:
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gr.Markdown("# Basic Agent Evaluation")
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gr.Markdown("Login with Hugging Face and click the button to run evaluation and submit your answers.")
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profile = gr.LoginButton()
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run_button = gr.Button("Run and Submit")
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status_output = gr.Textbox(label="Submission Status", lines=4)
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results_table = gr.DataFrame(label="Answers Submitted")
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run_button.click(fn=run_and_submit_all, inputs=[profile], outputs=[status_output, results_table])
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if __name__ == "__main__":
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demo.launch()
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