Renamed the file and made that G small of gradio

recipes/quickstart/inference/local_inference/multi_modal_infer_gradio_UI.py

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+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):
+    # Initialize cleaned_output variable
+    cleaned_output = ""
+
+    if image is not None:
+        # Resize image if necessary
+        image = image.resize(MAX_IMAGE_SIZE)
+        prompt = f"<|image|><|begin_of_text|>{user_prompt} Answer:"
+        # Preprocess the image and prompt
+        inputs = processor(image, prompt, return_tensors="pt").to(device)
+    else:
+        # Text-only input if no image is provided
+        prompt = f"<|begin_of_text|>{user_prompt} Answer:"
+        # Preprocess the prompt only (no image)
+        inputs = processor(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=1.0, 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(server_name="0.0.0.0", server_port=12003)
+demo.launch()