Update app.py

app.py

@@ -1,28 +1,29 @@
 import gradio as gr
 from groq import Groq
 import os
+import json
 
 # Initialize Groq client
 client = Groq(api_key=os.environ["GROQ_API_KEY"])
 
-def generate_tutor_output(subject, difficulty, student_input, model):
+def generate_tutor_output(subject, grade, student_input, model):
     prompt = f"""
-    You are an expert tutor in {subject} at the {difficulty} level. 
+    You are an expert tutor in {subject} for a {grade} grade student. 
     The student has provided the following input: "{student_input}"
     
     Please generate:
-    1. A brief, engaging lesson on the topic (2-3 paragraphs)
-    2. A thought-provoking question to check understanding
-    3. Constructive feedback on the student's input
+    1. A fun, engaging lesson (2-3 paragraphs) tailored to a {grade} grader's understanding.
+    2. A thought-provoking multiple-choice question (with 4 options: a, b, c, d) to test understanding.
+    3. Constructive feedback on the student's input.
     
-    Format your response as a JSON object with keys: "lesson", "question", "feedback"
+    Format your response as a JSON object with keys: "lesson", "question", "options", "correct_answer", "feedback"
     """
 
     completion = client.chat.completions.create(
         messages=[
             {
                 "role": "system",
-                "content": f"You are the world's best AI tutor, renowned for your ability to explain complex concepts in an engaging, clear, and memorable way and giving math examples. Your expertise in {subject} is unparalleled, and you're adept at tailoring your teaching to {difficulty} level students. Your goal is to not just impart knowledge, but to inspire a love for learning and critical thinking.",
+                "content": f"You are a fun, creative AI tutor for {grade} graders, expert in {subject}. You explain concepts in a simple, exciting way with relatable examples (like math problems for their age). Your goal is to spark curiosity and help students practice what they learn!",
             },
             {
                 "role": "user",
@@ -30,25 +31,32 @@ def generate_tutor_output(subject, difficulty, student_input, model):
             }
         ],
         model=model,
-        max_tokens=1000,
+        max_tokens=1200,
     )
 
     return completion.choices[0].message.content
 
-with gr.Blocks() as demo:
-    gr.Markdown("# 🎓  Mixtral")
+def check_answer(selected_answer, correct_answer):
+    if selected_answer == correct_answer:
+        return "🎉 Awesome job! You got it right! Keep rocking it!", 10
+    else:
+        return f"😅 Not quite! The correct answer was '{correct_answer}'. Try again next time!", 0
+
+with gr.Blocks(title="Learn & Practice 🚀") as demo:
+    gr.Markdown("# 🚀 Learn & Practice Zone (Grades 5-10)")
     
+    # Input Section
     with gr.Row():
         with gr.Column(scale=2):
             subject = gr.Dropdown(
                 ["Math", "Science", "History", "Geography", "Economics"], 
                 label="Subject", 
-                info="Choose the subject of your lesson"
+                info="Pick your favorite subject!"
             )
-            difficulty = gr.Radio(
-                ["Beginner", "Intermediate", "Advanced"], 
-                label="Difficulty Level", 
-                info="Select your proficiency level"
+            grade = gr.Dropdown(
+                ["5th Grade", "6th Grade", "7th Grade", "8th Grade", "9th Grade", "10th Grade"], 
+                label="Your Grade", 
+                info="Select your grade level"
             )
             model_select = gr.Dropdown(
                 [
@@ -56,44 +64,86 @@ with gr.Blocks() as demo:
                     "qwen-2.5-coder-32b",
                     "qwen-2.5-32b"
                 ],
-                label="AI Model",
+                label="AI Tutor Model",
                 value="mixtral-8x7b-32768",
-                info="Select the AI model to use"
+                info="Choose your AI tutor"
             )
             student_input = gr.Textbox(
-                placeholder="Type your query here...", 
-                label="Your Input", 
-                info="Enter the topic you want to learn"
+                placeholder="What do you want to learn today?", 
+                label="Your Question", 
+                info="Ask anything about the subject!"
             )
-            submit_button = gr.Button("Generate Lesson and Interactive Question", variant="primary")
+            submit_button = gr.Button("Get Lesson & Practice", variant="primary")
         
+        # Output Section
         with gr.Column(scale=3):
-            lesson_output = gr.Markdown(label="Lesson")
-            question_output = gr.Markdown(label="Comprehension Question")
-            feedback_output = gr.Markdown(label="Feedback")
-    
+            lesson_output = gr.Markdown(label="Your Lesson")
+            question_output = gr.Markdown(label="Test Your Skills")
+            options_output = gr.Radio(label="Choose an Answer", choices=[], visible=False)
+            feedback_output = gr.Markdown(label="Feedback on Your Question")
+            answer_feedback = gr.Markdown(label="Answer Feedback")
+            points = gr.Number(label="Your Points", value=0)
+
+    # Instructions
     gr.Markdown("""
-    ### How to Use
-    1. Select a subject from the dropdown.
-    2. Choose your difficulty level.
-    3. Select an AI model to power your lesson.
-    4. Enter the topic or question you'd like to explore.
-    5. Click 'Generate Lesson' to receive a personalized lesson, question, and feedback.
-    6. Review the AI-generated content to enhance your learning.
-    7. Feel free to ask follow-up questions or explore new topics!
+    ### How to Play & Learn
+    1. Pick a subject and your grade.
+    2. Choose an AI tutor model.
+    3. Ask a question or topic you’re curious about.
+    4. Read the fun lesson, then answer the question to test yourself.
+    5. Earn points for correct answers and keep learning!
     """)
-    
+
     def process_output(output):
         try:
-            parsed = eval(output)
-            return parsed["lesson"], parsed["question"], parsed["feedback"]
+            parsed = json.loads(output)
+            options = [f"{k}. {v}" for k, v in zip(["a", "b", "c", "d"], parsed["options"])]
+            return (
+                parsed["lesson"],
+                parsed["question"],
+                options,
+                parsed["correct_answer"],
+                parsed["feedback"]
+            )
         except:
-            return "Error parsing output", "No question available", "No feedback available"
-    
+            return (
+                "Error generating lesson",
+                "No question available",
+                [],
+                "",
+                "No feedback available"
+            )
+
+    def update_interface(subject, grade, student_input, model):
+        output = generate_tutor_output(subject, grade, student_input, model)
+        lesson, question, options, correct_answer, feedback = process_output(output)
+        return (
+            lesson,
+            question,
+            gr.update(choices=options, visible=True),
+            feedback,
+            "",  # Clear answer feedback
+            gr.update(value=0)  # Reset points
+        ), correct_answer
+
+    # State to store correct answer
+    correct_answer_state = gr.State()
+
     submit_button.click(
-        fn=lambda s, d, i, m: process_output(generate_tutor_output(s, d, i, m)),
-        inputs=[subject, difficulty, student_input, model_select],
-        outputs=[lesson_output, question_output, feedback_output]
+        fn=update_interface,
+        inputs=[subject, grade, student_input, model_select],
+        outputs=[lesson_output, question_output, options_output, feedback_output, answer_feedback, points],
+        _js="() => {document.querySelector('.output-container').scrollIntoView({behavior: 'smooth'})}"
+    ).then(
+        fn=lambda x: x,
+        inputs=[gr.State()],
+        outputs=[correct_answer_state]
+    )
+
+    options_output.change(
+        fn=check_answer,
+        inputs=[options_output, correct_answer_state],
+        outputs=[answer_feedback, points]
     )
 
 if __name__ == "__main__":