Update app.py

app.py

@@ -1,28 +1,29 @@
 import gradio as gr
-from groq import Groq
 import os
 import threading
 import base64
 from io import BytesIO
+from groq import Groq
 
-# Initialize Groq client (No need for Mistral API)
-client = Groq(api_key=os.environ["GROQ_API_KEY"])
+# 🔹 Initialize Groq API Client (FREE)
+groq_client = Groq(api_key=os.getenv("GROQ_API_KEY"))
 
-# Load Text-to-Image Models
+# 🔹 Load Text-to-Image Models (Restoring Multi-Image Generation)
 model1 = gr.load("models/prithivMLmods/SD3.5-Turbo-Realism-2.0-LoRA")
 model2 = gr.load("models/Purz/face-projection")
+model3 = gr.load("models/stablediffusion/stable-diffusion-xl")
 
-# Stop event for threading (image generation)
+# 🔹 Stop Event for Threading
 stop_event = threading.Event()
 
-# Convert PIL image to Base64
-def pil_to_base64(pil_image, image_format='jpeg'):
+# 🔹 Convert PIL image to Base64
+def pil_to_base64(pil_image, image_format="jpeg"):
     buffered = BytesIO()
     pil_image.save(buffered, format=image_format)
-    base64_string = base64.b64encode(buffered.getvalue()).decode('utf-8')
+    base64_string = base64.b64encode(buffered.getvalue()).decode("utf-8")
     return base64_string, image_format
 
-# Function for Visual Question Answering (Groq)
+# 🔹 Function for Visual Question Answering (VQA) with Mixtral-8x7B
 def answer_question(text, image, temperature=0.0, max_tokens=1024):
     base64_string, file_format = pil_to_base64(image)
 
@@ -36,8 +37,8 @@ def answer_question(text, image, temperature=0.0, max_tokens=1024):
         }
     ]
     
-    chat_response = client.chat.completions.create(
-        model="gemma2-9b-it",  # Groq model for vision tasks
+    chat_response = groq_client.chat.completions.create(
+        model="mixtral-8x7b-32768",
         messages=messages,
         temperature=temperature,
         max_tokens=max_tokens
@@ -45,18 +46,24 @@ def answer_question(text, image, temperature=0.0, max_tokens=1024):
 
     return chat_response.choices[0].message.content
 
+# 🔹 Function to Generate Three Images (Multi-Output)
+def generate_images(prompt):
+    stop_event.clear()
+    img1 = model1.predict(prompt)
+    img2 = model2.predict(prompt)
+    img3 = model3.predict(prompt)
+    return img1, img2, img3
 
-# Clear all fields
+# 🔹 Clear All Fields
 def clear_all():
-    return "", None, ""
-
+    return "", None, "", None, None, None
 
-# Set up the Gradio interface
+# 🔹 Set up Gradio Interface
 with gr.Blocks() as demo:
-    gr.Markdown("# 🎓 AI Tutor & Visual Learning Assistant")
+    gr.Markdown("# 🎓 AI Tutor, VQA & Image Generation")
 
-    # Section 3: Visual Question Answering (Groq)
-    gr.Markdown("## 🖼️ Visual Question Answering (Groq)")
+    # 🔹 Section 1: Visual Question Answering (Groq)
+    gr.Markdown("## 🖼️ Visual Question Answering (Mixtral-8x7B)")
     with gr.Row():
         with gr.Column(scale=2):
             question = gr.Textbox(placeholder="Ask about the image...", lines=2)
@@ -66,24 +73,42 @@ 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="Groq VQA Response")
+            output_text = gr.Textbox(lines=10, label="Mixtral VQA Response")
     
     with gr.Row():
         clear_btn = gr.Button("Clear", variant="secondary")
         submit_btn_vqa = gr.Button("Submit", variant="primary")
 
-    # VQA Processing
+    # 🔹 Section 2: Image Generation (3 Outputs)
+    gr.Markdown("## 🎨 AI-Generated Images (3 Variations)")
+    with gr.Row():
+        prompt = gr.Textbox(placeholder="Describe the image you want...", lines=2)
+        generate_btn = gr.Button("Generate Images", variant="primary")
+
+    with gr.Row():
+        image1 = gr.Image(label="Image 1")
+        image2 = gr.Image(label="Image 2")
+        image3 = gr.Image(label="Image 3")
+
+    # 🔹 VQA Processing
     submit_btn_vqa.click(
         fn=answer_question,
         inputs=[question, image, temperature, max_tokens],
         outputs=[output_text]
     )
 
-    # Clear VQA Inputs
+    # 🔹 Image Generation Processing
+    generate_btn.click(
+        fn=generate_images,
+        inputs=[prompt],
+        outputs=[image1, image2, image3]
+    )
+
+    # 🔹 Clear All Inputs
     clear_btn.click(
         fn=clear_all,
         inputs=[],
-        outputs=[question, image, output_text]
+        outputs=[question, image, output_text, image1, image2, image3]
     )
 
 if __name__ == "__main__":