Update app.py

app.py

@@ -1,9 +1,8 @@
 import gradio as gr
-from groq import Groq
-import os
 
-# Initialize Groq client with your API key
-client = Groq(api_key=os.environ["GROQ_API_KEY"])
+# Load the models correctly
+model1 = gr.load("models/prithivMLmods/SD3.5-Turbo-Realism-2.0-LoRA")
+model2 = gr.load("models/Purz/face-projection")
 
 def generate_tutor_output(subject, difficulty, student_input):
     prompt = f"""
@@ -18,32 +17,25 @@ 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",  # Model for text generation
-        max_tokens=1000,
-    )
-    
-    return completion.choices[0].message.content
+    # 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."
+    }
 
-# 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.
+# 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})
+    else:
+        # Use model1 for SD3.5-Turbo-Realism-2.0-LoRA for general visual generation
+        image = model1.generate({"prompt": 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(" ", "+")
+    return image
 
 # Set up the Gradio interface
 with gr.Blocks() as demo:
@@ -78,6 +70,7 @@ with gr.Blocks() as demo:
     # Separate section for visual generation
     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")
 
@@ -94,12 +87,9 @@ with gr.Blocks() as demo:
 
     # Function to process lesson and pass the topic for visual generation
     def process_lesson_output(subject, difficulty, student_input):
-        try:
-            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", ""
+        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(
@@ -111,7 +101,7 @@ with gr.Blocks() as demo:
     # Generate Visual Button
     generate_visual_button.click(
         fn=generate_visual,
-        inputs=topic_for_visual,
+        inputs=[topic_for_visual, use_face_projection],
         outputs=visual_output
     )