app

README.md

@@ -5,6 +5,7 @@ colorFrom: pink
 colorTo: pink
 sdk: gradio
 sdk_version: 5.49.1
+python_version: 3.13
 app_file: app.py
 pinned: false
 short_description: Review Your Practice Quiz

app.py

@@ -0,0 +1,113 @@
+import gradio as gr
+from openai import OpenAI
+from dotenv import load_dotenv
+import os
+import json
+load_dotenv()
+
+def login(username, password):
+    return (username=="admin" and password=="NRSG4604")
+
+def build_system_prompt(params: dict) -> str:
+    question = params.get("question", "")
+    raw_choices = params.get("choices", "[]")
+    try:
+        choices = json.loads(raw_choices)
+    except json.JSONDecodeError:
+        choices = []
+
+    student_answer = params.get("student_answer", "")
+    correct_answer = params.get("correct_answer", "")
+
+    # You can tune this prompt however you like
+    lines = []
+    lines.append("You are a tutoring assistant helping a student review a Canvas quiz question.")
+    if question:
+        lines.append(f"\nQuestion:\n{question}")
+    if choices:
+        lines.append("\nChoices:")
+        for i, c in enumerate(choices):
+            label = chr(ord("A") + i)
+            lines.append(f"{label}. {c}")
+    if student_answer:
+        lines.append(f"\nStudent's answer: {student_answer}")
+    if correct_answer:
+        lines.append(f"Correct answer: {correct_answer}")
+
+    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.")
+    return "\n".join(lines)
+
+def predict(message, messages, request: gr.Request):
+    if request is not None and hasattr(request, "query_params"):
+        query_params = dict(request.query_params)
+    else:
+        return messages
+
+    # 2. On the first turn, inject a system prompt built from the quiz data
+    if len(messages) == 0:
+        system_prompt = build_system_prompt(query_params)
+        messages.append({"role": "system", "content": system_prompt})
+    messages.append({"role": "user", "content": message})
+    params = {
+        "model": "gpt-5",
+        "messages": messages,
+        "stream": True,
+        "stream_options": {"include_usage": True},
+    }
+
+    response = client.chat.completions.create(**params)
+    content = ""
+    for event in response:
+        if event.choices and event.choices[0].delta.content:
+            chunk = event.choices[0].delta.content
+            content += chunk
+            yield content
+
+    messages.append(
+        {
+            "role": "assistant",
+            "content": content,
+        }
+    )
+    return messages
+
+def vote(data: gr.LikeData):
+    if data.liked:
+        print("You upvoted this response: " + data.value["value"])
+    else:
+        print("You downvoted this response: " + data.value["value"])
+
+
+def show_question(request: gr.Request):
+    params = dict(request.query_params)
+    q = params.get("question", "")
+    return f"### Question\n\n{q}" if q else "No question data found in URL."
+
+
+api_key=os.getenv('api')
+client = OpenAI(api_key=api_key)
+placeholder = """
+<center><h1>Hello there!</h1><br> 
+How can I help you?
+</center>
+"""
+
+examples=["Can you explain why my answer is wrong?", "Can you explain this concept?"]
+
+with gr.Blocks(title="Chat") as demo:
+    question_md = gr.Markdown()
+    chatbot=gr.Chatbot(
+        placeholder=placeholder,
+        type='messages',
+    )
+    #chatbot.like(vote, None, None)
+    chat = gr.ChatInterface(
+        predict,
+        chatbot=chatbot,
+        type="messages",
+        examples=examples,
+        cache_examples=False,
+        flagging_mode="manual"
+    )
+    demo.load(show_question, inputs=None, outputs=question_md)
+    demo.launch(auth=login, ssr_mode=False)

requirements.txt

@@ -0,0 +1,2 @@
+openai
+dotenv