removed old files after renaming and added markup safe in readme for CI/CD

recipes/quickstart/inference/local_inference/multi_modal_infer_Gradio_UI.py

@@ -1,151 +0,0 @@
-import gradio as gr
-import torch
-import os
-from PIL import Image
-from accelerate import Accelerator
-from transformers import MllamaForConditionalGeneration, AutoProcessor
-import argparse  # Import argparse
-
-# Parse the command line arguments
-parser = argparse.ArgumentParser(description="Run Gradio app with Hugging Face model")
-parser.add_argument("--hf_token", type=str, required=True, help="Hugging Face authentication token")
-args = parser.parse_args()
-
-# Hugging Face token
-hf_token = args.hf_token
-
-# Initialize Accelerator
-accelerate = Accelerator()
-device = accelerate.device
-
-# Set memory management for PyTorch
-os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'max_split_size_mb:128'  # or adjust size as needed
-
-# Model ID
-model_id = "meta-llama/Llama-3.2-11B-Vision-Instruct"
-
-# Load model with the Hugging Face token
-model = MllamaForConditionalGeneration.from_pretrained(
-    model_id,
-    torch_dtype=torch.bfloat16,
-    device_map=device,
-    use_auth_token=hf_token  # Pass the Hugging Face token here
-)
-
-# Load the processor
-processor = AutoProcessor.from_pretrained(model_id, use_auth_token=hf_token)
-
-# Visual theme
-visual_theme = gr.themes.Default()  # Default, Soft or Monochrome
-
-# Constants
-MAX_OUTPUT_TOKENS = 2048
-MAX_IMAGE_SIZE = (1120, 1120)
-
-# Function to process the image and generate a description
-def describe_image(image, user_prompt, temperature, top_k, top_p, max_tokens, history):
-    # Resize image if necessary
-    image = image.resize(MAX_IMAGE_SIZE)
-
-    # Initialize cleaned_output variable
-    cleaned_output = ""
-
-    prompt = f"<|image|><|begin_of_text|>{user_prompt} Answer:"
-    # Preprocess the image and prompt
-    inputs = processor(image, prompt, return_tensors="pt").to(device)
-
-    # Generate output with model
-    output = model.generate(
-        **inputs,
-        max_new_tokens=min(max_tokens, MAX_OUTPUT_TOKENS),
-        temperature=temperature,
-        top_k=top_k,
-        top_p=top_p
-    )
-
-    # Decode the raw output
-    raw_output = processor.decode(output[0])
-    
-    # Clean up the output to remove system tokens
-    cleaned_output = raw_output.replace("<|image|><|begin_of_text|>", "").strip().replace(" Answer:", "")
-
-    
-    # Ensure the prompt is not repeated in the output
-    if cleaned_output.startswith(user_prompt):
-        cleaned_output = cleaned_output[len(user_prompt):].strip()
-        
-    # Append the new conversation to the history
-    history.append((user_prompt, cleaned_output))
-
-    return history
-
-# Function to clear the chat history
-def clear_chat():
-    return []
-
-# Gradio Interface
-def gradio_interface():
-    with gr.Blocks(visual_theme) as demo:
-        gr.HTML(
-        """
-    <h1 style='text-align: center'>
-    meta-llama/Llama-3.2-11B-Vision-Instruct
-    </h1>
-    """)
-        with gr.Row():
-            # Left column with image and parameter inputs
-            with gr.Column(scale=1):
-                image_input = gr.Image(
-                    label="Image", 
-                    type="pil", 
-                    image_mode="RGB", 
-                    height=512,  # Set the height
-                    width=512   # Set the width
-                )
-
-                # Parameter sliders
-                temperature = gr.Slider(
-                    label="Temperature", minimum=0.1, maximum=2.0, value=0.6, step=0.1, interactive=True)
-                top_k = gr.Slider(
-                    label="Top-k", minimum=1, maximum=100, value=50, step=1, interactive=True)
-                top_p = gr.Slider(
-                    label="Top-p", minimum=0.1, maximum=1.0, value=0.9, step=0.1, interactive=True)
-                max_tokens = gr.Slider(
-                    label="Max Tokens", minimum=50, maximum=MAX_OUTPUT_TOKENS, value=100, step=50, interactive=True)
-
-            # Right column with the chat interface
-            with gr.Column(scale=2):
-                chat_history = gr.Chatbot(label="Chat", height=512)
-
-                # User input box for prompt
-                user_prompt = gr.Textbox(
-                    show_label=False,
-                    container=False,
-                    placeholder="Enter your prompt", 
-                    lines=2
-                )
-
-                # Generate and Clear buttons
-                with gr.Row():
-                    generate_button = gr.Button("Generate")
-                    clear_button = gr.Button("Clear")
-
-                # Define the action for the generate button
-                generate_button.click(
-                    fn=describe_image, 
-                    inputs=[image_input, user_prompt, temperature, top_k, top_p, max_tokens, chat_history],
-                    outputs=[chat_history]
-                )
-
-                # Define the action for the clear button
-                clear_button.click(
-                    fn=clear_chat,
-                    inputs=[],
-                    outputs=[chat_history]
-                )
-
-    return demo
-
-# Launch the interface
-demo = gradio_interface()
-demo.launch()

requirements.txt

@@ -25,4 +25,5 @@ faiss-gpu; python_version < '3.11'
 unstructured[pdf]
 sentence_transformers
 codeshield
-gradio
+gradio
+markupsafe==2.0.1