Update app.py

app.py

@@ -1,19 +1,10 @@
 import gradio as gr
 from groq import Groq
 import os
-import threading  # Import threading module
 
 # Initialize Groq client with your API key
 client = Groq(api_key=os.environ["GROQ_API_KEY"])
 
-# Load Text-to-Image Models
-model1 = gr.load("models/prithivMLmods/SD3.5-Turbo-Realism-2.0-LoRA")
-model2 = gr.load("models/Purz/face-projection")
-
-# Stop event for threading (image generation)
-stop_event = threading.Event()
-
-# Function to generate tutor output (lesson, question, feedback)
 def generate_tutor_output(subject, difficulty, student_input):
     prompt = f"""
     You are an expert tutor in {subject} at the {difficulty} level. 
@@ -44,31 +35,19 @@ def generate_tutor_output(subject, difficulty, student_input):
     
     return completion.choices[0].message.content
 
-# Function to generate images based on model selection
-def generate_images(text, selected_model):
-    stop_event.clear()
-
-    if selected_model == "Model 1 (Turbo Realism)":
-        model = model1
-    elif selected_model == "Model 2 (Face Projection)":
-        model = model2
-    else:
-        return ["Invalid model selection."] * 3
-
-    results = []
-    for i in range(3):
-        if stop_event.is_set():
-            return ["Image generation stopped by user."] * 3
-
-        modified_text = f"{text} variation {i+1}"
-        result = model(modified_text)
-        results.append(result)
-
-    return results
+# Function to generate visual output (image) based on the lesson/topic
+def generate_visual(topic):
+    # You can integrate your image generation model here. 
+    # For simplicity, let's assume you have an image generation model available.
+    # Here's an example where we generate a simple placeholder image based on the topic.
+    
+    # Example image generation logic (you can replace this with your actual model).
+    # For now, returning a placeholder image.
+    return "https://via.placeholder.com/500x300.png?text=" + topic.replace(" ", "+")
 
 # Set up the Gradio interface
 with gr.Blocks() as demo:
-    gr.Markdown("# 🎓 Your AI Tutor with Visuals & Images")
+    gr.Markdown("# 🎓 Your AI Tutor")
     
     with gr.Row():
         with gr.Column(scale=2):
@@ -88,46 +67,52 @@ with gr.Blocks() as demo:
                 label="Your Input", 
                 info="Enter the topic you want to learn"
             )
-            model_selector = gr.Radio(
-                ["Model 1 (Turbo Realism)", "Model 2 (Face Projection)"],
-                label="Select Image Generation Model",
-                value="Model 1 (Turbo Realism)"
-            )
-            submit_button = gr.Button("Generate Lesson & Images", variant="primary")
+            generate_lesson_button = gr.Button("Generate Lesson", variant="primary")
         
         with gr.Column(scale=3):
-            # Output fields for lesson, question, feedback, and images
+            # 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")
-            output1 = gr.Image(label="Generated Image 1")
-            output2 = gr.Image(label="Generated Image 2")
-            output3 = gr.Image(label="Generated Image 3")
     
+    # Separate section for visual generation
+    with gr.Row():
+        topic_for_visual = gr.Textbox(label="Topic for Visual", placeholder="Generated Topic")
+        generate_visual_button = gr.Button("Generate Visual Output")
+        visual_output = gr.Image(label="Generated Visual")
+
+    # Markdown instructions
     gr.Markdown("""
     ### How to Use
     1. Select a subject from the dropdown.
     2. Choose your difficulty level.
     3. Enter the topic or question you'd like to explore.
-    4. Choose the model for image generation.
-    5. Click 'Generate Lesson & Images' to receive a personalized lesson, question, feedback, and images.
+    4. Click 'Generate Lesson' to receive a personalized lesson, question, and feedback.
+    5. After generating the lesson, you can click 'Generate Visual Output' to create a related visual representation.
     6. Review the AI-generated content to enhance your learning.
-    7. Feel free to ask follow-up questions or explore new topics!
     """)
-    
-    def process_output(subject, difficulty, student_input, selected_model):
+
+    # Function to process lesson and pass the topic for visual generation
+    def process_lesson_output(subject, difficulty, student_input):
         try:
-            tutor_output = generate_tutor_output(subject, difficulty, student_input)
-            parsed = eval(tutor_output)  # Convert string to dictionary
-            images = generate_images(student_input, selected_model)  # Generate images
-            return parsed["lesson"], parsed["question"], parsed["feedback"], images[0], images[1], images[2]
-        except:
-            return "Error parsing output", "No question available", "No feedback available", None, None, None
-    
-    submit_button.click(
-        fn=process_output,
-        inputs=[subject, difficulty, student_input, model_selector],
-        outputs=[lesson_output, question_output, feedback_output, output1, output2, output3]
+            parsed = eval(generate_tutor_output(subject, difficulty, student_input))  # Convert string to dictionary
+            # Store the lesson topic (you can use the lesson or question as a topic for visual generation)
+            return parsed["lesson"], parsed["question"], parsed["feedback"], student_input
+        except Exception as e:
+            return f"Error processing output: {e}", "No question available", "No feedback available", ""
+
+    # Generate Lesson Button
+    generate_lesson_button.click(
+        fn=process_lesson_output,
+        inputs=[subject, difficulty, student_input],
+        outputs=[lesson_output, question_output, feedback_output, topic_for_visual]
+    )
+
+    # Generate Visual Button
+    generate_visual_button.click(
+        fn=generate_visual,
+        inputs=topic_for_visual,
+        outputs=visual_output
     )
 
 if __name__ == "__main__":