Update app.py

app.py

@@ -1,20 +1,11 @@
 import gradio as gr
 from groq import Groq
 import os
-import threading  # Import threading module
 
-# Initialize Groq client with your API key
+# Initialize Groq client
 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):
+def generate_tutor_output(subject, difficulty, student_input, model):
     prompt = f"""
     You are an expert tutor in {subject} at the {difficulty} level. 
     The student has provided the following input: "{student_input}"
@@ -26,53 +17,31 @@ 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": 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
+        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. Your goal is to not just impart knowledge, but to inspire a love for learning and critical thinking.",
+            },
+            {
+                "role": "user",
+                "content": prompt,
+            }
+        ],
+        model=model,
         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
+    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 for generating Text-based output (lesson, question, feedback)
+    gr.Markdown("# 🎓 Your AI Tutor by Farhan")
+    
     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"], 
+                ["Math", "Science", "History", "Geography", "Economics"], 
                 label="Subject", 
                 info="Choose the subject of your lesson"
             )
@@ -81,71 +50,51 @@ with gr.Blocks() as demo:
                 label="Difficulty Level", 
                 info="Select your proficiency level"
             )
+            model_select = gr.Dropdown(
+                [
+                    "mixtral-8x7b-32768",
+                    "qwen-2.5-coder-32b",
+                    "qwen-2.5-32b"
+                ],
+                label="AI Model",
+                value="mixtral-8x7b-32768",
+                info="Select the AI model to use"
+            )
             student_input = gr.Textbox(
                 placeholder="Type your query here...", 
                 label="Your Input", 
                 info="Enter the topic you want to learn"
             )
-            submit_button_text = gr.Button("Generate Lesson & Question", variant="primary")
+            submit_button = gr.Button("Generate Lesson", 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. **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!
+    1. Select a subject from the dropdown.
+    2. Choose your difficulty level.
+    3. Select an AI model to power your lesson.
+    4. Enter the topic or question you'd like to explore.
+    5. Click 'Generate Lesson' to receive a personalized lesson, question, and feedback.
+    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_text(subject, difficulty, student_input):
+    def process_output(output):
         try:
-            tutor_output = generate_tutor_output(subject, difficulty, student_input)
-            parsed = eval(tutor_output)  # Convert string to dictionary
+            parsed = eval(output)
             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],
+    submit_button.click(
+        fn=lambda s, d, i, m: process_output(generate_tutor_output(s, d, i, m)),
+        inputs=[subject, difficulty, student_input, model_select],
         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)