Update utils.py

utils.py

@@ -5,25 +5,24 @@ import os
 # Initialize Groq client
 client = Groq(api_key=os.environ["GROQ_API_KEY"])
 
-def generate_tutor_output(subject, difficulty, chapter, student_input):
+def generate_tutor_output(chapter, difficulty, student_input):
     prompt = f"""
-    You are an expert tutor in {subject} at the {difficulty} level. 
-    The student has provided the following chapter: "{chapter}" and input: "{student_input}"
+    You are an expert 10th-grade physics tutor at the {difficulty} level. 
+    The student has selected the following chapter: "{chapter}" and provided this input: "{student_input}"
     
-    Please generate:
-    1. A brief, engaging lesson on the chapter topic (2-3 paragraphs)
-    2. A thought-provoking question to check understanding
-    3. Constructive feedback on the student's input
-    4. A short test based on the chapter content
+    Please generate an unsolved test with:
+    1. Multiple Choice Questions (MCQs)
+    2. Short Answer Questions
+    3. Long Answer Questions
     
-    Format your response as a JSON object with keys: "lesson", "question", "feedback", "test"
+    Format your response as a JSON object with keys: "mcqs", "short_questions", "long_questions"
     """
 
     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 physics examples."
+                "content": "You are the world's best AI tutor specializing in 10th-grade physics, adept at creating customized tests. Generate MCQs, short questions, and long questions based on the provided chapter."
             },
             {
                 "role": "user",
@@ -34,22 +33,18 @@ def generate_tutor_output(subject, difficulty, chapter, student_input):
         max_tokens=1000,
     )
 
+    print("Raw Model Output:", completion.choices[0].message.content)  # Debugging output
     return completion.choices[0].message.content
 
 with gr.Blocks() as demo:
-    gr.Markdown("# 🎓 Your AI Tutor by Farhan")
+    gr.Markdown("# 🎓 10th Grade Physics Tutor - Test Generator")
     
     with gr.Row():
         with gr.Column(scale=2):
-            subject = gr.Dropdown(
-                ["Physics", "Math", "Science", "History", "Literature", "Code", "AI"], 
-                label="Subject", 
-                info="Choose the subject of your lesson"
-            )
             chapter = gr.Dropdown(
                 ["Chapter 1: Kinematics", "Chapter 2: Dynamics", "Chapter 3: Energy", "Chapter 4: Waves"], 
-                label="Chapter", 
-                info="Select the chapter to generate a test"
+                label="Select Chapter", 
+                info="Choose the chapter for the test"
             )
             difficulty = gr.Radio(
                 ["Beginner", "Intermediate", "Advanced"], 
@@ -57,41 +52,59 @@ with gr.Blocks() as demo:
                 info="Select your proficiency level"
             )
             student_input = gr.Textbox(
-                placeholder="Type your query here...", 
+                placeholder="Enter additional details...", 
                 label="Your Input", 
-                info="Enter the topic you want to learn"
+                info="Specify 'MCQs', 'short questions', or 'long questions' if needed"
             )
-            submit_button = gr.Button("Generate Lesson and Test", variant="primary")
+            submit_button = gr.Button("Generate Test", variant="primary")
         
         with gr.Column(scale=3):
-            lesson_output = gr.Markdown(label="Lesson")
-            question_output = gr.Markdown(label="Comprehension Question")
-            feedback_output = gr.Markdown(label="Feedback")
-            test_output = gr.Markdown(label="Generated Test")
-
+            mcq_output = gr.Markdown(label="MCQs")
+            short_question_output = gr.Markdown(label="Short Questions")
+            long_question_output = gr.Markdown(label="Long Questions")
+    
     gr.Markdown(""" 
-    ### How to Use 
-    1. Select a subject from the dropdown. 
-    2. Choose a chapter for the test. 
-    3. Select your difficulty level. 
-    4. Enter the topic or question you'd like to explore. 
-    5. Click 'Generate Lesson and Test' to receive personalized content.
-    6. Review the AI-generated lesson, question, feedback, and test.
-    7. Feel free to ask follow-up questions or explore new topics!
+    ### Instructions
+    1. Select a chapter to generate questions from.
+    2. Choose your difficulty level.
+    3. Provide any additional details if needed.
+    4. Specify 'MCQs', 'short questions', or 'long questions' to filter the output, or leave blank for all types.
+    5. Click 'Generate Test' to receive questions based on the chapter.
     """)
 
-    def process_output(output):
+    def process_output(output, user_input):
         try:
+            # Parse the JSON output
             parsed = eval(output)
-            return parsed["lesson"], parsed["question"], parsed["feedback"], parsed["test"]
-        except:
-            return "Error parsing output", "No question available", "No feedback available", "No test available"
+            
+            # Filter based on the user input
+            mcqs, short_questions, long_questions = "", "", ""
+            
+            if "mcq" in user_input.lower():
+                mcqs = "\n".join([f"**Q:** {mcq['question']} - Options: {', '.join(mcq['options'])}" for mcq in parsed.get("mcqs", [])])
+            
+            if "short" in user_input.lower():
+                short_questions = "\n".join([sq["question"] for sq in parsed.get("short_questions", [])])
+            
+            if "long" in user_input.lower():
+                long_questions = "\n".join([lq["question"] for lq in parsed.get("long_questions", [])])
+            
+            # Default to all types if no specific type is mentioned
+            if not any(keyword in user_input.lower() for keyword in ["mcq", "short", "long"]):
+                mcqs = "\n".join([f"**Q:** {mcq['question']} - Options: {', '.join(mcq['options'])}" for mcq in parsed.get("mcqs", [])])
+                short_questions = "\n".join([sq["question"] for sq in parsed.get("short_questions", [])])
+                long_questions = "\n".join([lq["question"] for lq in parsed.get("long_questions", [])])
+
+            return mcqs or "No MCQs available", short_questions or "No short questions available", long_questions or "No long questions available"
+        
+        except Exception as e:
+            print("Error processing output:", e)  # Additional error message for debugging
+            return "Error parsing output", "No short questions available", "No long questions available"
     
     submit_button.click(
-        fn=lambda s, d, c, i: process_output(generate_tutor_output(s, d, c, i)),
-        inputs=[subject, difficulty, chapter, student_input],
-        outputs=[lesson_output, question_output, feedback_output, test_output]
+        fn=lambda c, d, i: process_output(generate_tutor_output(c, d, i), i),
+        inputs=[chapter, difficulty, student_input],
+        outputs=[mcq_output, short_question_output, long_question_output]
     )
 
-if __name__ == "__main__":
-    demo.launch(server_name="0.0.0.0", server_port=7860)
+if __name__ == "__main__":