From 64537458d10a2c4c4f7981799234d97c55597d7b Mon Sep 17 00:00:00 2001
From: himanshushukla12 <himanshushukla.shukla3@gmail.com>
Date: Sun, 27 Oct 2024 19:11:40 +0000
Subject: [PATCH] removed old files after renaming and added markup safe in
readme for CI/CD
---
.../multi_modal_infer_Gradio_UI.py | 151 ------------------
requirements.txt | 3 +-
2 files changed, 2 insertions(+), 152 deletions(-)
delete mode 100644 recipes/quickstart/inference/local_inference/multi_modal_infer_Gradio_UI.py
diff --git a/recipes/quickstart/inference/local_inference/multi_modal_infer_Gradio_UI.py b/recipes/quickstart/inference/local_inference/multi_modal_infer_Gradio_UI.py
deleted file mode 100644
index a2c1db19..00000000
--- a/recipes/quickstart/inference/local_inference/multi_modal_infer_Gradio_UI.py
+++ /dev/null
@@ -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()
diff --git a/requirements.txt b/requirements.txt
index 9b4a2a92..a5d218b8 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -25,4 +25,5 @@ faiss-gpu; python_version < '3.11'
unstructured[pdf]
sentence_transformers
codeshield
-gradio
\ No newline at end of file
+gradio
+markupsafe==2.0.1
--
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