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import json
import os
from transformers import AutoProcessor, AutoModelForVision2Seq
import torch
from PIL import Image
import gradio as gr
import subprocess
from llava.model.builder import load_pretrained_model
from llava.mm_utils import get_model_name_from_path
from llava.eval.run_llava import eval_model
os.chdir("My_new_LLaVA/LLaVA") # Update this if needed
# Verify the current working directory
print("Current Working Directory:", os.getcwd())
# Load the LLaVA model and processor
llava_model_path = "/My_new_LLaVA/llava-fine_tune_model"
# Load the LLaVA-Med model and processor
llava_med_model_path = "/My_new_LLaVA/llava-fine_tune_model"
# Args class to store arguments for LLaVA models
class Args:
def __init__(self, model_path, model_base, model_name, query, image_path, conv_mode, image_file, sep, temperature, top_p, num_beams, max_new_tokens):
self.model_path = model_path
self.model_base = model_base
self.model_name = model_name
self.query = query
self.image_path = image_path
self.conv_mode = conv_mode
self.image_file = image_file
self.sep = sep
self.temperature = temperature
self.top_p = top_p
self.num_beams = num_beams
self.max_new_tokens = max_new_tokens
# Function to predict using LLaVA
def predict_llava(image, question, temperature, max_tokens):
# Save the image temporarily
image.save("temp_image.jpg")
# Setup evaluation arguments
args = Args(
model_path=llava_model_path,
model_base=None,
model_name=get_model_name_from_path(llava_model_path),
query=question,
image_path="temp_image.jpg",
conv_mode=None,
image_file="temp_image.jpg",
sep=",",
temperature=temperature,
top_p=None,
num_beams=1,
max_new_tokens=max_tokens
)
# Generate the answer using the selected model
output = eval_model(args)
return output
# Function to predict using LLaVA-Med
def predict_llava_med(image, question, temperature, max_tokens):
# Save the image temporarily
image_path = "temp_image_med.jpg"
image.save(image_path)
# Command to run the LLaVA-Med model
command = [
"python", "-m", "llava.eval.run_llava",
"--model-name", llava_med_model_path,
"--image-file", image_path,
"--query", question,
"--temperature", str(temperature),
"--max-new-tokens", str(max_tokens)
]
# Execute the command and capture the output
result = subprocess.run(command, capture_output=True, text=True)
return result.stdout.strip() # Return the output as text
# Main prediction function
def predict(model_name, image, text, temperature, max_tokens):
if model_name == "LLaVA":
return predict_llava(image, text, temperature, max_tokens)
elif model_name == "LLaVA-Med":
return predict_llava_med(image, text, temperature, max_tokens)
# Define the Gradio interface
interface = gr.Interface(
fn=predict,
inputs=[
gr.Radio(choices=["LLaVA", "LLaVA-Med"], label="Select Model"),
gr.Image(type="pil", label="Input Image"),
gr.Textbox(label="Input Text"),
gr.Slider(minimum=0.1, maximum=1.0, default=0.7, label="Temperature"),
gr.Slider(minimum=1, maximum=512, default=256, label="Max Tokens"),
],
outputs=gr.Textbox(label="Output Text"),
title="Multimodal LLM Interface",
description="Switch between models and adjust parameters.",
)
# Launch the Gradio interface
interface.launch()
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