Update app.py

app.py

@@ -2,45 +2,21 @@ import gradio as gr
 from groq import Groq
 import os
 import json
-import random
-import sqlite3
 
 # Initialize Groq client
 client = Groq(api_key=os.environ["GROQ_API_KEY"])
 print("API Key:", os.environ.get("GROQ_API_KEY"))  # Debug print
 
-# Define valid_models globally
+# Define valid models (only those starting with "qwen" or "mistral")
 valid_models = [
-    "distil-whisper-large-v3-en",
-    "gemma2-9b-it",
-    "llama-3.3-70b-versatile",
-    "llama-3.1-8b-instant",
-    "llama-guard-3-8b",
-    "llama3-70b-8192",
-    "llama3-8b-8192",
-    "mixtral-8x7b-32768",
-    "whisper-large-v3",
-    "whisper-large-v3-turbo",
     "qwen-qwq-32b",
-    "mistral-saba-24b",
     "qwen-2.5-coder-32b",
     "qwen-2.5-32b",
     "deepseek-r1-distill-qwen-32b",
-    "deepseek-r1-distill-llama-70b-specdec",
-    "deepseek-r1-distill-llama-70b",
-    "llama-3.3-70b-specdec",
-    "llama-3.2-1b-preview",
-    "llama-3.2-3b-preview",
-    "llama-3.2-11b-vision-preview",
-    "llama-3.2-90b-vision-preview"
+    "mixtral-8x7b-32768",
+    "mistral-saba-24b"
 ]
 
-# Initialize or connect to SQLite database for points
-conn = sqlite3.connect("student_points.db", check_same_thread=False)
-cursor = conn.cursor()
-cursor.execute('''CREATE TABLE IF NOT EXISTS points (student_id TEXT, points INTEGER, timestamp TEXT)''')
-conn.commit()
-
 def generate_tutor_output(subject, grade, student_input, model):
     if model not in valid_models:
         model = "mixtral-8x7b-32768"  # Fallback model
@@ -51,11 +27,11 @@ def generate_tutor_output(subject, grade, student_input, model):
     The student has provided the following input: "{student_input}"
     
     Please generate:
-    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.
+    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
     
-    Format your response as a JSON object with keys: "lesson", "question", "options", "correct_answer", "feedback"
+    Format your response as a JSON object with keys: "lesson", "question", "feedback"
     """
 
     try:
@@ -63,7 +39,7 @@ def generate_tutor_output(subject, grade, student_input, model):
             messages=[
                 {
                     "role": "system",
-                    "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!",
+                    "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 with examples suitable for {grade} graders. Your expertise in {subject} is unparalleled, and you're adept at tailoring your teaching to {grade} grade students. Your goal is to not just impart knowledge, but to inspire a love for learning and critical thinking.",
                 },
                 {
                     "role": "user",
@@ -71,7 +47,7 @@ def generate_tutor_output(subject, grade, student_input, model):
                 }
             ],
             model=model,
-            max_tokens=1200,
+            max_tokens=1000,
         )
         return completion.choices[0].message.content
     except Exception as e:
@@ -79,60 +55,18 @@ def generate_tutor_output(subject, grade, student_input, model):
         return json.dumps({
             "lesson": f"Error: Could not generate lesson. API error: {str(e)}",
             "question": "No question available",
-            "options": [],
-            "correct_answer": "",
             "feedback": "No feedback available due to API error"
         })
 
-def check_answer(selected_answer, correct_answer, current_points, student_id="student1"):
-    if selected_answer == correct_answer:
-        feedback = "🎉 Awesome job! You got it right! Keep rocking it!"
-        new_points = current_points + 10
-    else:
-        feedback = f"😅 Not quite! The correct answer was '{correct_answer}'. Try again next time!"
-        new_points = current_points
-    # Save points to database
-    cursor.execute("INSERT INTO points (student_id, points, timestamp) VALUES (?, ?, ?)", 
-                   (student_id, new_points, "2025-03-08 04:25"))
-    conn.commit()
-    return feedback, new_points
-
-def process_output(output):
-    print(f"Raw API Output: {output}")
-    try:
-        parsed = json.loads(output)
-        # Shuffle options for variety
-        options_list = list(zip(["a", "b", "c", "d"], parsed["options"]))
-        random.shuffle(options_list)
-        options = [f"{k}. {v}" for k, v in options_list]
-        correct_key = [k for k, v in options_list if v == parsed["correct_answer"]][0]
-        return (
-            parsed["lesson"],
-            parsed["question"],
-            options,
-            correct_key,
-            parsed["feedback"]
-        )
-    except Exception as e:
-        print(f"JSON Parsing Error: {str(e)}")
-        return (
-            f"Error parsing response: {str(e)}",
-            "No question available",
-            [],
-            "",
-            "No feedback available"
-        )
-
-with gr.Blocks(title="Learn & Practice 🚀") as demo:
-    gr.Markdown("# 🚀 Learn & Practice Zone (Grades 5-10)")
+with gr.Blocks() as demo:
+    gr.Markdown("# 🎓 Learn & Explore (Grades 5-10)")
     
-    # Input Section
     with gr.Row():
         with gr.Column(scale=2):
             subject = gr.Dropdown(
                 ["Math", "Science", "History", "Geography", "Economics"], 
                 label="Subject", 
-                info="Pick your favorite subject!"
+                info="Choose the subject of your lesson"
             )
             grade = gr.Dropdown(
                 ["5th Grade", "6th Grade", "7th Grade", "8th Grade", "9th Grade", "10th Grade"], 
@@ -141,70 +75,47 @@ with gr.Blocks(title="Learn & Practice 🚀") as demo:
             )
             model_select = gr.Dropdown(
                 valid_models,
-                label="AI Tutor Model",
+                label="AI Model",
                 value="mixtral-8x7b-32768",
-                info="Choose your AI tutor"
+                info="Select the AI model to use"
             )
             student_input = gr.Textbox(
-                placeholder="What do you want to learn today?", 
-                label="Your Question", 
-                info="Ask anything about the subject!"
+                placeholder="Type your query here...", 
+                label="Your Input", 
+                info="Enter the topic you want to learn"
             )
-            submit_button = gr.Button("Get Lesson & Practice", variant="primary")
+            submit_button = gr.Button("Generate Lesson and Question", variant="primary")
         
-        # Output Section
         with gr.Column(scale=3):
-            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
+            lesson_output = gr.Markdown(label="Lesson")
+            question_output = gr.Markdown(label="Comprehension Question")
+            feedback_output = gr.Markdown(label="Feedback")
+    
     gr.Markdown("""
-    ### 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!
+    ### How to Use
+    1. Select a subject from the dropdown.
+    2. Choose your grade 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!
     """)
-
-    def update_interface(subject, grade, student_input, model):
-        print(f"Selected Model: {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 for new session
-        ), correct_answer
-
-    # State to store correct answer
-    correct_answer_state = gr.State()
-
+    
+    def process_output(output):
+        print(f"Raw API Output: {output}")  # Debug print
+        try:
+            parsed = json.loads(output)
+            return parsed["lesson"], parsed["question"], parsed["feedback"]
+        except Exception as e:
+            print(f"JSON Parsing Error: {str(e)}")
+            return "Error parsing output", "No question available", "No feedback available"
+    
     submit_button.click(
-        fn=update_interface,
+        fn=lambda s, g, i, m: process_output(generate_tutor_output(s, g, i, m)),
         inputs=[subject, grade, student_input, model_select],
-        outputs=[lesson_output, question_output, options_output, feedback_output, answer_feedback, points]
-    ).then(
-        fn=lambda x: x,
-        inputs=[gr.State()],
-        outputs=[correct_answer_state]
-    )
-
-    options_output.change(
-        fn=check_answer,
-        inputs=[options_output, correct_answer_state, points],
-        outputs=[answer_feedback, points]
+        outputs=[lesson_output, question_output, feedback_output]
     )
 
 if __name__ == "__main__":
-    try:
-        demo.launch(server_name="0.0.0.0", server_port=7860)
-    finally:
-        conn.close()  # Close database connection on shutdown
+    demo.launch(server_name="0.0.0.0", server_port=7860)