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Zero
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import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, AutoProcessor, AutoConfig, AutoModel
from PIL import Image
import logging
from transformers import BitsAndBytesConfig
# Setup logging
logging.basicConfig(level=logging.INFO)
class LLaVAPhiModel:
def __init__(self, model_id="sagar007/Lava_phi"):
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
logging.info(f"Using device: {self.device}")
# Initialize quantization config
quantization_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.float16,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4"
)
try:
# Load model directly from Hugging Face Hub
logging.info(f"Loading model from {model_id}...")
self.model = AutoModelForCausalLM.from_pretrained(
model_id,
quantization_config=quantization_config,
device_map="auto",
torch_dtype=torch.bfloat16,
trust_remote_code=True
)
self.tokenizer = AutoTokenizer.from_pretrained(model_id)
# Set up padding token
if self.tokenizer.pad_token is None:
self.tokenizer.pad_token = self.tokenizer.eos_token
self.model.config.pad_token_id = self.tokenizer.eos_token_id
# Load CLIP model and processor
logging.info("Loading CLIP model and processor...")
self.processor = AutoProcessor.from_pretrained("openai/clip-vit-base-patch32")
self.clip = AutoModel.from_pretrained("openai/clip-vit-base-patch32").to(self.device)
# Store conversation history
self.history = []
except Exception as e:
logging.error(f"Error initializing model: {str(e)}")
raise
def process_image(self, image):
"""Process image through CLIP"""
with torch.no_grad():
image_inputs = self.processor(images=image, return_tensors="pt")
image_features = self.clip.get_image_features(
pixel_values=image_inputs.pixel_values.to(self.device)
)
return image_features
def generate_response(self, message, image=None):
try:
if image is not None:
# Get image features
image_features = self.process_image(image)
# Format prompt
prompt = f"human: <image>\n{message}\ngpt:"
# Add context from history
context = ""
for turn in self.history[-3:]:
context += f"human: {turn[0]}\ngpt: {turn[1]}\n"
full_prompt = context + prompt
# Prepare text inputs
inputs = self.tokenizer(
full_prompt,
return_tensors="pt",
padding=True,
truncation=True,
max_length=512
)
inputs = {k: v.to(self.device) for k, v in inputs.items()}
# Add image features to inputs
inputs["image_features"] = image_features
# Generate response
with torch.no_grad():
outputs = self.model.generate(
**inputs,
max_new_tokens=256,
min_length=20,
temperature=0.7,
do_sample=True,
top_p=0.9,
top_k=40,
repetition_penalty=1.5,
no_repeat_ngram_size=3,
use_cache=True,
pad_token_id=self.tokenizer.pad_token_id,
eos_token_id=self.tokenizer.eos_token_id
)
else:
# Text-only response
prompt = f"human: {message}\ngpt:"
context = ""
for turn in self.history[-3:]:
context += f"human: {turn[0]}\ngpt: {turn[1]}\n"
full_prompt = context + prompt
inputs = self.tokenizer(
full_prompt,
return_tensors="pt",
padding=True,
truncation=True,
max_length=512
)
inputs = {k: v.to(self.device) for k, v in inputs.items()}
with torch.no_grad():
outputs = self.model.generate(
**inputs,
max_new_tokens=150,
min_length=20,
temperature=0.6,
do_sample=True,
top_p=0.85,
top_k=30,
repetition_penalty=1.8,
no_repeat_ngram_size=4,
use_cache=True,
pad_token_id=self.tokenizer.pad_token_id,
eos_token_id=self.tokenizer.eos_token_id
)
# Decode response
response = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
# Clean up response
if "gpt:" in response:
response = response.split("gpt:")[-1].strip()
if "human:" in response:
response = response.split("human:")[0].strip()
if "<image>" in response:
response = response.replace("<image>", "").strip()
# Update history
self.history.append((message, response))
return response
except Exception as e:
logging.error(f"Error generating response: {str(e)}")
logging.error(f"Full traceback:", exc_info=True)
return f"Error: {str(e)}"
def clear_history(self):
self.history = []
return None
def create_demo():
# Initialize model
model = LLaVAPhiModel()
with gr.Blocks(css="footer {visibility: hidden}") as demo:
gr.Markdown(
"""
# LLaVA-Phi Demo
Chat with a vision-language model that can understand both text and images.
"""
)
chatbot = gr.Chatbot(height=400)
with gr.Row():
with gr.Column(scale=0.7):
msg = gr.Textbox(
show_label=False,
placeholder="Enter text and/or upload an image",
container=False
)
with gr.Column(scale=0.15, min_width=0):
clear = gr.Button("Clear")
with gr.Column(scale=0.15, min_width=0):
submit = gr.Button("Submit", variant="primary")
image = gr.Image(type="pil", label="Upload Image (Optional)")
def respond(message, chat_history, image):
if not message and image is None:
return chat_history
response = model.generate_response(message, image)
chat_history.append((message, response))
return "", chat_history
def clear_chat():
model.clear_history()
return None, None
submit.click(
respond,
[msg, chatbot, image],
[msg, chatbot],
)
clear.click(
clear_chat,
None,
[chatbot, image],
)
msg.submit(
respond,
[msg, chatbot, image],
[msg, chatbot],
)
return demo
if __name__ == "__main__":
demo = create_demo()
demo.launch(
server_name="0.0.0.0",
server_port=7860,
share=True
) |