Update app.py

app.py

@@ -1,9 +1,19 @@
 import gradio as gr
+from groq import Groq
+import os
+import threading  # Import threading module
 
-# Load the models correctly
+# 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. 
@@ -17,33 +27,50 @@ def generate_tutor_output(subject, difficulty, student_input):
     Format your response as a JSON object with keys: "lesson", "question", "feedback"
     """
     
-    # Assume this function is already integrated with Groq or another model for text generation.
-    # We can replace this with a mock lesson response or keep it as it is.
-    # The lesson is passed as a return from your other model for lesson generation.
-    return {
-        "lesson": "This is your lesson content based on the input topic.",
-        "question": "What is your understanding of this concept?",
-        "feedback": "Your explanation needs more clarity on the topic."
-    }
+    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 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,
+        }],
+        model="mixtral-8x7b-32768",  # Model for text generation
+        max_tokens=1000,
+    )
+    
+    return completion.choices[0].message.content
+
+# Function to generate images based on model selection
+def generate_images(text, selected_model):
+    stop_event.clear()
 
-# Function to generate visual output using model1 or model2
-def generate_visual(topic, use_face_projection=False):
-    if use_face_projection:
-        # Use model2 for face projection
-        image = model2.generate({"prompt": topic})
+    if selected_model == "Model 1 (Turbo Realism)":
+        model = model1
+    elif selected_model == "Model 2 (Face Projection)":
+        model = model2
     else:
-        # Use model1 for SD3.5-Turbo-Realism-2.0-LoRA for general visual generation
-        image = model1.generate({"prompt": topic})
-    
-    return image
+        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
 
 # Set up the Gradio interface
 with gr.Blocks() as demo:
-    gr.Markdown("# 🎓 Your AI Tutor")
-    
+    gr.Markdown("# 🎓 Your AI Tutor with Visuals & Images")
+
+    # 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
+            # Input fields for subject, difficulty, and student input for textual output
             subject = gr.Dropdown(
                 ["Math", "Science", "History", "Literature", "Code", "AI"], 
                 label="Subject", 
@@ -59,7 +86,7 @@ with gr.Blocks() as demo:
                 label="Your Input", 
                 info="Enter the topic you want to learn"
             )
-            generate_lesson_button = gr.Button("Generate Lesson", variant="primary")
+            submit_button_text = gr.Button("Generate Lesson & Question", variant="primary")
         
         with gr.Column(scale=3):
             # Output fields for lesson, question, and feedback
@@ -67,42 +94,57 @@ with gr.Blocks() as demo:
             question_output = gr.Markdown(label="Comprehension Question")
             feedback_output = gr.Markdown(label="Feedback")
     
-    # Separate section for visual generation
+    # Section for generating Visual output
     with gr.Row():
-        topic_for_visual = gr.Textbox(label="Topic for Visual", placeholder="Generated Topic")
-        use_face_projection = gr.Checkbox(label="Use Face Projection?", value=False)
-        generate_visual_button = gr.Button("Generate Visual Output")
-        visual_output = gr.Image(label="Generated Visual")
-
-    # Markdown instructions
+        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",
+                value="Model 1 (Turbo Realism)"
+            )
+            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")
+    
     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. 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.
+    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!
     """)
-
-    # Function to process lesson and pass the topic for visual generation
-    def process_lesson_output(subject, difficulty, student_input):
-        parsed = generate_tutor_output(subject, difficulty, student_input)  # Get the lesson output
-        # 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
-
-    # Generate Lesson Button
-    generate_lesson_button.click(
-        fn=process_lesson_output,
+    
+    def process_output_text(subject, difficulty, student_input):
+        try:
+            tutor_output = generate_tutor_output(subject, difficulty, student_input)
+            parsed = eval(tutor_output)  # Convert string to dictionary
+            return parsed["lesson"], parsed["question"], parsed["feedback"]
+        except:
+            return "Error parsing output", "No question available", "No feedback available"
+    
+    def process_output_visual(text, selected_model):
+        try:
+            images = generate_images(text, selected_model)  # Generate images
+            return images[0], images[1], images[2]
+        except:
+            return None, None, None
+    
+    # Generate Text-based Output
+    submit_button_text.click(
+        fn=process_output_text,
         inputs=[subject, difficulty, student_input],
-        outputs=[lesson_output, question_output, feedback_output, topic_for_visual]
+        outputs=[lesson_output, question_output, feedback_output]
     )
-
-    # Generate Visual Button
-    generate_visual_button.click(
-        fn=generate_visual,
-        inputs=[topic_for_visual, use_face_projection],
-        outputs=visual_output
+    
+    # Generate Visual Output
+    submit_button_visual.click(
+        fn=process_output_visual,
+        inputs=[student_input, model_selector],
+        outputs=[output1, output2, output3]
     )
 
 if __name__ == "__main__":