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import gradio as gr |
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from openai import OpenAI |
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from dotenv import load_dotenv |
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import os |
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import json |
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load_dotenv() |
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def login(username, password): |
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return (username=="admin" and password=="NRSG4604") |
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def build_system_prompt(params: dict) -> str: |
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question = params.get("question", "") |
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raw_choices = params.get("choices", "[]") |
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try: |
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choices = json.loads(raw_choices) |
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except json.JSONDecodeError: |
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choices = [] |
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student_answer = params.get("student_answer", "") |
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correct_answer = params.get("correct_answer", "") |
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lines = [] |
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lines.append("You are a tutoring assistant helping a student review a Canvas quiz question.") |
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if question: |
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lines.append(f"\nQuestion:\n{question}") |
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if choices: |
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lines.append("\nChoices:") |
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for i, c in enumerate(choices): |
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label = chr(ord("A") + i) |
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lines.append(f"{label}. {c}") |
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if student_answer: |
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lines.append(f"\nStudent's answer: {student_answer}") |
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if correct_answer: |
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lines.append(f"Correct answer: {correct_answer}") |
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lines.append("\n\nWhen the student asks something, explain step-by-step why the correct answer is correct and, if relevant, why the student's answer is incorrect. Be supportive and focus on reasoning, not just telling them the answer.") |
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return "\n".join(lines) |
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def predict(message, messages, request: gr.Request): |
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if request is not None and hasattr(request, "query_params"): |
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query_params = dict(request.query_params) |
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else: |
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return messages |
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if len(messages) == 0: |
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system_prompt = build_system_prompt(query_params) |
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messages.append({"role": "system", "content": system_prompt}) |
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messages.append({"role": "user", "content": message}) |
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params = { |
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"model": "gpt-5", |
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"messages": messages, |
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"stream": True, |
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"stream_options": {"include_usage": True}, |
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} |
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response = client.chat.completions.create(**params) |
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content = "" |
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for event in response: |
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if event.choices and event.choices[0].delta.content: |
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chunk = event.choices[0].delta.content |
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content += chunk |
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yield content |
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messages.append( |
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{ |
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"role": "assistant", |
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"content": content, |
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} |
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) |
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return messages |
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def vote(data: gr.LikeData): |
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if data.liked: |
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print("You upvoted this response: " + data.value["value"]) |
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else: |
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print("You downvoted this response: " + data.value["value"]) |
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def show_question(request: gr.Request): |
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params = dict(request.query_params) |
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q = params.get("question", "") |
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return f"### Question\n\n{q}" if q else "No question data found in URL." |
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api_key=os.getenv('api') |
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client = OpenAI(api_key=api_key) |
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placeholder = """ |
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<center><h1>Hello there!</h1><br> |
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How can I help you? |
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</center> |
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""" |
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examples=["Can you explain why my answer is wrong?", "Can you explain this concept?"] |
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with gr.Blocks(title="Chat") as demo: |
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question_md = gr.Markdown() |
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chatbot=gr.Chatbot( |
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placeholder=placeholder, |
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type='messages', |
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) |
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chat = gr.ChatInterface( |
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predict, |
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chatbot=chatbot, |
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type="messages", |
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examples=examples, |
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cache_examples=False, |
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flagging_mode="manual" |
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) |
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demo.load(show_question, inputs=None, outputs=question_md) |
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demo.launch(auth=login, ssr_mode=False) |
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