Update app.py

app.py

@@ -1,9 +1,9 @@
 import gradio as gr
-from groq import Groq  # Correct import
+from groq import Groq
 import os
-import threading
+import threading  # Import threading module
 
-# Initialize Groq client
+# Initialize Groq client with your API key
 client = Groq(api_key=os.environ["GROQ_API_KEY"])
 
 # Load Text-to-Image Models
@@ -30,7 +30,7 @@ def generate_tutor_output(subject, difficulty, student_input):
     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. Your expertise in {subject} is unparalleled, and you're adept at tailoring your teaching to {difficulty} level students."
+            "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."
         }, {
             "role": "user",
             "content": prompt,
@@ -63,40 +63,14 @@ def generate_images(text, selected_model):
 
     return results
 
-# New function for processing visual input
-def process_visual_input(image, task, question=""):
-    """Processes the uploaded image based on the selected task."""
-    if task == "Image Captioning":
-        prompt = "Describe this image in detail."
-    elif task == "OCR (Text Extraction)":
-        prompt = "Extract all readable text from this image."
-    elif task == "Visual Question Answering":
-        prompt = f"Answer this question based on the image: {question}"
-    else:
-        return "Invalid task selected."
-
-    # Sending image + prompt to the model
-    completion = client.chat.completions.create(
-        messages=[{
-            "role": "system",
-            "content": "You are an expert AI that analyzes images and provides captions, extracts text, or answers visual questions."
-        }, {
-            "role": "user",
-            "content": prompt,
-        }],
-        model="llava-1.5-7b",  # Using a vision-language model
-        max_tokens=500,
-    )
-
-    return completion.choices[0].message.content
-
 # Set up the Gradio interface
 with gr.Blocks() as demo:
     gr.Markdown("# 🎓 Your AI Tutor with Visuals & Images")
 
-    # Section 1: Text-based Learning (Lesson, Question, Feedback)
+    # Section for generating Text-based output (lesson, question, feedback)
     with gr.Row():
         with gr.Column(scale=2):
+            # Input fields for subject, difficulty, and student input for textual output
             subject = gr.Dropdown(
                 ["Math", "Science", "History", "Literature", "Code", "AI"], 
                 label="Subject", 
@@ -115,13 +89,15 @@ with gr.Blocks() as demo:
             submit_button_text = gr.Button("Generate Lesson & Question", variant="primary")
         
         with gr.Column(scale=3):
+            # Output fields for lesson, question, and feedback
             lesson_output = gr.Markdown(label="Lesson")
             question_output = gr.Markdown(label="Comprehension Question")
             feedback_output = gr.Markdown(label="Feedback")
     
-    # Section 2: Text-based Image Generation
+    # Section for generating Visual output
     with gr.Row():
         with gr.Column(scale=2):
+            # Input fields for text and model selection for image generation
             model_selector = gr.Radio(
                 ["Model 1 (Turbo Realism)", "Model 2 (Face Projection)"],
                 label="Select Image Generation Model",
@@ -130,40 +106,18 @@ with gr.Blocks() as demo:
             submit_button_visual = gr.Button("Generate Visuals", variant="primary")
         
         with gr.Column(scale=3):
+            # Output fields for generated images
             output1 = gr.Image(label="Generated Image 1")
             output2 = gr.Image(label="Generated Image 2")
             output3 = gr.Image(label="Generated Image 3")
-
-    # Section 3: Visual Input Processing
-    with gr.Row():
-        with gr.Column(scale=2):
-            image_input = gr.Image(label="Upload an Image", type="filepath")
-            task_selector = gr.Radio(
-                ["Image Captioning", "OCR (Text Extraction)", "Visual Question Answering"],
-                label="Select Image Processing Task",
-                value="Image Captioning"
-            )
-            question_input = gr.Textbox(
-                placeholder="Enter question (only for VQA)", 
-                label="Question (Optional)",
-                visible=False
-            )
-            submit_button_visual_input = gr.Button("Process Image", variant="primary")
-        
-        with gr.Column(scale=3):
-            visual_output = gr.Markdown(label="Image Analysis Result")
-
-    # Toggle visibility of question input for VQA
-    def toggle_question_visibility(task):
-        return gr.update(visible=(task == "Visual Question Answering"))
-
-    task_selector.change(
-        fn=toggle_question_visibility, 
-        inputs=[task_selector], 
-        outputs=[question_input]
-    )
-
-    # Process text-based learning
+    
+    gr.Markdown("""
+    ### How to Use
+    1. **Text Section**: Select a subject and difficulty, type your query, and click 'Generate Lesson & Question' to get your personalized lesson, comprehension question, and feedback.
+    2. **Visual Section**: Select the model for image generation, then click 'Generate Visuals' to receive 3 variations of an image based on your topic.
+    3. Review the AI-generated content to enhance your learning experience!
+    """)
+    
     def process_output_text(subject, difficulty, student_input):
         try:
             tutor_output = generate_tutor_output(subject, difficulty, student_input)
@@ -172,27 +126,21 @@ with gr.Blocks() as demo:
         except:
             return "Error parsing output", "No question available", "No feedback available"
     
-    # Process image generation
     def process_output_visual(text, selected_model):
         try:
-            images = generate_images(text, selected_model)
+            images = generate_images(text, selected_model)  # Generate images
             return images[0], images[1], images[2]
         except:
             return None, None, None
-
-    # Process visual input (image)
-    submit_button_visual_input.click(
-        fn=process_visual_input,
-        inputs=[image_input, task_selector, question_input],
-        outputs=[visual_output]
-    )
-
+    
+    # Generate Text-based Output
     submit_button_text.click(
         fn=process_output_text,
         inputs=[subject, difficulty, student_input],
         outputs=[lesson_output, question_output, feedback_output]
     )
     
+    # Generate Visual Output
     submit_button_visual.click(
         fn=process_output_visual,
         inputs=[student_input, model_selector],