Update app.py

app.py

@@ -1,10 +1,19 @@
 import gradio as gr
 from groq import Groq
 import os
+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
+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. 
@@ -17,29 +26,51 @@ def generate_tutor_output(subject, difficulty, student_input):
     
     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="mixtral-8x7b-32768",
+        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()
+
+    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
+
+# Set up the Gradio interface
 with gr.Blocks() as demo:
-    gr.Markdown("# 🎓 Your AI Tutor by Farhan")
-    
+    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 for textual output
             subject = gr.Dropdown(
                 ["Math", "Science", "History", "Literature", "Code", "AI"], 
                 label="Subject", 
@@ -55,35 +86,66 @@ with gr.Blocks() as demo:
                 label="Your Input", 
                 info="Enter the topic you want to learn"
             )
-            submit_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
             lesson_output = gr.Markdown(label="Lesson")
             question_output = gr.Markdown(label="Comprehension Question")
             feedback_output = gr.Markdown(label="Feedback")
     
+    # 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",
+                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. Review the AI-generated content to enhance your learning.
-    6. Feel free to ask follow-up questions or explore new topics!
+    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(output):
+    def process_output_text(subject, difficulty, student_input):
         try:
-            parsed = eval(output)
+            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"
     
-    submit_button.click(
-        fn=lambda s, d, i: process_output(generate_tutor_output(s, d, i)),
+    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]
     )
+    
+    # Generate Visual Output
+    submit_button_visual.click(
+        fn=process_output_visual,
+        inputs=[student_input, model_selector],
+        outputs=[output1, output2, output3]
+    )
 
 if __name__ == "__main__":
-    demo.launch(server_name="0.0.0.0", server_port=7860)
+    demo.launch(server_name="0.0.0.0", server_port=7860)