Update app.py

app.py

@@ -1,87 +1,52 @@
 import gradio as gr
 from groq import Groq
 import os
-from functools import lru_cache
 
-# Initialize Groq client with error handling
-if "GROQ_API_KEY" not in os.environ:
-    raise ValueError("GROQ_API_KEY not found in environment variables.")
+# Initialize Groq client
 client = Groq(api_key=os.environ["GROQ_API_KEY"])
 
-class EducationalAgent:
-    def __init__(self, subject, specialization):
-        self.subject = subject
-        self.specialization = specialization
-
-    @lru_cache(maxsize=100)  # Simple caching mechanism for repeated queries
-    def generate_response(self, prompt, grade_level):
-        try:
-            completion = client.chat.completions.create(
-                messages=[
-                    {
-                        "role": "system",
-                        "content": f"You are an AI tutor specialized in {self.subject}. Your focus is on {self.specialization}. Adjust your explanations for a {grade_level} level. Provide your response in the following format:\n\nLesson: [Your lesson content here]\n\nQuestion: [A comprehension question related to the lesson]\n\nFeedback: [General feedback or tips related to the topic]",
-                    },
-                    {"role": "user", "content": prompt},
-                ],
-                model="llama3-groq-8b-8192-tool-use-preview",
-                max_tokens=4000,
-                temperature=0.7,
-            )
-            return completion.choices[0].message.content
-        except Exception as e:
-            return f"An error occurred: {str(e)}"
-
-# Create educational agent instances
-math_agent = EducationalAgent("Mathematics", "algebra, geometry, and calculus")
-science_agent = EducationalAgent("Science", "physics, chemistry, and biology")
-literature_agent = EducationalAgent("Literature", "literary analysis and writing skills")
-history_agent = EducationalAgent("History", "world history and historical analysis")
-
-def process_query(query, subject, grade_level):
-    if len(query.strip()) == 0:
-        return "Please enter a question.", "", ""
-    if len(query) > 500:
-        return "Please limit your question to 500 characters.", "", ""
+def generate_tutor_output(subject, difficulty, student_input):
+    prompt = f"""
+    You are an expert tutor in {subject} at the {difficulty} level. 
+    The student has provided the following input: "{student_input}"
     
-    # Choose the correct agent based on subject
-    if subject == "Mathematics":
-        response = math_agent.generate_response(query, grade_level)
-    elif subject == "Science":
-        response = science_agent.generate_response(query, grade_level)
-    elif subject == "Literature":
-        response = literature_agent.generate_response(query, grade_level)
-    elif subject == "History":
-        response = history_agent.generate_response(query, grade_level)
-    else:
-        return "Invalid subject selected.", "", ""
+    Please generate:
+    1. A brief, engaging lesson on the topic (2-3 paragraphs)
+    2. A thought-provoking examples with answers to check understanding
+    3. Give real world problems that can be solved with the lesson 
     
-    # Parse the response
-    lesson, question, feedback = "No lesson available", "No question available", "No feedback available"
-    parts = response.split("\n\n")
-    for part in parts:
-        if part.startswith("Lesson:"):
-            lesson = part.replace("Lesson:", "").strip()
-        elif part.startswith("Question:"):
-            question = part.replace("Question:", "").strip()
-        elif part.startswith("Feedback:"):
-            feedback = part.replace("Feedback:", "").strip()
-    
-    return lesson, question, feedback
+    Format your response as a JSON object with keys: "lesson", "question", "feedback"
+    """
+
+    completion = client.chat.completions.create(
+        messages=[
+            {
+                "role": "system",
+                "content": "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.",
+            },
+            {
+                "role": "user",
+                "content": prompt,
+            }
+        ],
+        model="llama3-groq-8b-8192-tool-use-preview",
+        max_tokens=1000,
+    )
+
+    return completion.choices[0].message.content
 
-# Create Gradio interface
 with gr.Blocks() as demo:
     gr.Markdown("# 🎓 Vers3Dynamics Tutor: Your Personal Learning Companion")
-
+    
     with gr.Row():
         with gr.Column(scale=2):
             subject = gr.Dropdown(
-                ["Mathematics", "Science", "History", "Literature"], 
+                ["Math", "Science", "History", "Literature"], 
                 label="Subject", 
                 info="Choose the subject of your lesson"
             )
             difficulty = gr.Radio(
-                ["Elementary", "Middle School", "High School", "College"], 
+                ["Beginner", "Intermediate", "Advanced"], 
                 label="Difficulty Level", 
                 info="Select your proficiency level"
             )
@@ -96,7 +61,7 @@ with gr.Blocks() as demo:
             lesson_output = gr.Markdown(label="Lesson")
             question_output = gr.Markdown(label="Comprehension Question")
             feedback_output = gr.Markdown(label="Feedback")
-
+    
     gr.Markdown("""
     ### How to Use
     1. Select a subject from the dropdown.
@@ -106,10 +71,17 @@ with gr.Blocks() as demo:
     5. Review the AI-generated content to enhance your learning.
     6. Feel free to ask follow-up questions or explore new topics!
     """)
-
+    
+    def process_output(output):
+        try:
+            parsed = eval(output)
+            return parsed["lesson"], parsed["question"], parsed["feedback"]
+        except:
+            return "Error parsing output", "No question available", "No feedback available"
+    
     submit_button.click(
-        fn=process_query,
-        inputs=[student_input, subject, difficulty],
+        fn=lambda s, d, i: process_output(generate_tutor_output(s, d, i)),
+        inputs=[subject, difficulty, student_input],
         outputs=[lesson_output, question_output, feedback_output]
     )