Update app.py

app.py

@@ -4,15 +4,10 @@ import os
 import threading
 import base64
 from io import BytesIO
-from mistralai import Mistral  # Pixtral-12B integration
 
-# Initialize Groq client
+# Initialize Groq client (No need for Mistral API)
 client = Groq(api_key=os.environ["GROQ_API_KEY"])
 
-# Initialize Mistral AI client (Pixtral-12B-2409 for VQA)
-mistral_client = Mistral(api_key=os.environ["MISTRAL_API_KEY"])
-pixtral_model = "pixtral-12b-2409"
-
 # Load Text-to-Image Models
 model1 = gr.load("models/prithivMLmods/SD3.5-Turbo-Realism-2.0-LoRA")
 model2 = gr.load("models/Purz/face-projection")
@@ -20,7 +15,6 @@ model2 = gr.load("models/Purz/face-projection")
 # Stop event for threading (image generation)
 stop_event = threading.Event()
 
-
 # Convert PIL image to Base64
 def pil_to_base64(pil_image, image_format='jpeg'):
     buffered = BytesIO()
@@ -28,57 +22,7 @@ def pil_to_base64(pil_image, image_format='jpeg'):
     base64_string = base64.b64encode(buffered.getvalue()).decode('utf-8')
     return base64_string, image_format
 
-
-# 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. 
-    The student has provided the following input: "{student_input}"
-    
-    Please generate:
-    1. A brief, engaging lesson on the topic (2-3 paragraphs)
-    2. A thought-provoking question to check understanding
-    3. Constructive feedback on the student's 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 explaining complex concepts with clarity and 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",
-        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()
-
-    model = model1 if selected_model == "Model 1 (Turbo Realism)" else model2 if selected_model == "Model 2 (Face Projection)" else None
-    if not model:
-        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 for Visual Question Answering (Pixtral-12B)
+# Function for Visual Question Answering (Groq)
 def answer_question(text, image, temperature=0.0, max_tokens=1024):
     base64_string, file_format = pil_to_base64(image)
 
@@ -92,8 +36,8 @@ def answer_question(text, image, temperature=0.0, max_tokens=1024):
         }
     ]
     
-    chat_response = mistral_client.chat.complete(
-        model=pixtral_model,
+    chat_response = client.chat.completions.create(
+        model="gemma2-9b-it",  # Groq model for vision tasks
         messages=messages,
         temperature=temperature,
         max_tokens=max_tokens
@@ -111,36 +55,8 @@ def clear_all():
 with gr.Blocks() as demo:
     gr.Markdown("# 🎓 AI Tutor & Visual Learning Assistant")
 
-    # Section 1: Text-based Learning
-    with gr.Row():
-        with gr.Column(scale=2):
-            subject = gr.Dropdown(["Math", "Science", "History", "Literature", "Code", "AI"], label="Subject")
-            difficulty = gr.Radio(["Beginner", "Intermediate", "Advanced"], label="Difficulty Level")
-            student_input = gr.Textbox(placeholder="Type your query here...", label="Your Input")
-            submit_button_text = gr.Button("Generate Lesson & Question", variant="primary")
-        
-        with gr.Column(scale=3):
-            lesson_output = gr.Markdown(label="Lesson")
-            question_output = gr.Markdown(label="Comprehension Question")
-            feedback_output = gr.Markdown(label="Feedback")
-    
-    # Section 2: Image Generation
-    with gr.Row():
-        with gr.Column(scale=2):
-            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):
-            output1 = gr.Image(label="Generated Image 1")
-            output2 = gr.Image(label="Generated Image 2")
-            output3 = gr.Image(label="Generated Image 3")
-    
-    # Section 3: Visual Question Answering (Pixtral-12B)
-    gr.Markdown("## 🖼️ Visual Question Answering (Pixtral-12B)")
+    # Section 3: Visual Question Answering (Groq)
+    gr.Markdown("## 🖼️ Visual Question Answering (Groq)")
     with gr.Row():
         with gr.Column(scale=2):
             question = gr.Textbox(placeholder="Ask about the image...", lines=2)
@@ -150,26 +66,12 @@ with gr.Blocks() as demo:
                 max_tokens = gr.Slider(label="Max Tokens", minimum=128, maximum=2048, value=1024, step=128)
         
         with gr.Column(scale=3):
-            output_text = gr.Textbox(lines=10, label="Pixtral 12B Response")
+            output_text = gr.Textbox(lines=10, label="Groq VQA Response")
     
     with gr.Row():
         clear_btn = gr.Button("Clear", variant="secondary")
         submit_btn_vqa = gr.Button("Submit", variant="primary")
 
-    # Generate Text-based Output
-    submit_button_text.click(
-        fn=lambda subject, difficulty, student_input: eval(generate_tutor_output(subject, difficulty, student_input)),
-        inputs=[subject, difficulty, student_input],
-        outputs=[lesson_output, question_output, feedback_output]
-    )
-    
-    # Generate Visual Output
-    submit_button_visual.click(
-        fn=generate_images,
-        inputs=[student_input, model_selector],
-        outputs=[output1, output2, output3]
-    )
-
     # VQA Processing
     submit_btn_vqa.click(
         fn=answer_question,