A Space for Vision/Multimodal
✨ Tips: Send Messages or upload 1 IMAGE/FILE per time.
✨ Tips: Please increase MAX LENGTH when deal with file.
🤙 Supported Format: pdf, txt, docx, pptx, md, png, jpg, webp
🙇♂️ May be rebuilding from time to time.
"""
CSS = """
h1 {
text-align: center;
display: block;
}
"""
model = AutoModelForCausalLM.from_pretrained(
MODEL_ID,
torch_dtype=torch.bfloat16,
low_cpu_mem_usage=True,
trust_remote_code=True
).to(0)
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
model.eval()
def extract_text(path):
return open(path, 'r').read()
def extract_pdf(path):
doc = pymupdf.open(path)
text = ""
for page in doc:
text += page.get_text()
return text
def extract_docx(path):
doc = docx.Document(path)
data = []
for paragraph in doc.paragraphs:
data.append(paragraph.text)
content = '\n\n'.join(data)
return content
def extract_pptx(path):
prs = Presentation(path)
text = ""
for slide in prs.slides:
for shape in slide.shapes:
if hasattr(shape, "text"):
text += shape.text + "\n"
return text
def mode_load(path):
choice = ""
file_type = path.split(".")[-1]
print(file_type)
if file_type in ["pdf", "txt", "py", "docx", "pptx", "json", "cpp", "md"]:
if file_type.endswith("pdf"):
content = extract_pdf(path)
elif file_type.endswith("docx"):
content = extract_docx(path)
elif file_type.endswith("pptx"):
content = extract_pptx(path)
else:
content = extract_text(path)
choice = "doc"
print(content[:100])
return choice, content[:5000]
elif file_type in ["png", "jpg", "jpeg", "bmp", "tiff", "webp"]:
content = Image.open(path).convert('RGB')
choice = "image"
return choice, content
else:
raise gr.Error("Oops, unsupported files.")
@spaces.GPU()
def stream_chat(message, history: list, temperature: float, max_length: int, top_p: float, top_k: int, penalty: float):
print(f'message is - {message}')
print(f'history is - {history}')
conversation = []
prompt_files = []
if message["files"]:
choice, contents = mode_load(message["files"][-1])
if choice == "image":
conversation.append({"role": "user", "image": contents, "content": message['text']})
elif choice == "doc":
format_msg = contents + "\n\n\n" + "{} files uploaded.\n" + message['text']
conversation.append({"role": "user", "content": format_msg})
else:
if len(history) == 0:
#raise gr.Error("Please upload an image first.")
contents = None
conversation.append({"role": "user", "content": message['text']})
else:
#image = Image.open(history[0][0][0])
for prompt, answer in history:
if answer is None:
prompt_files.append(prompt[0])
conversation.extend([{"role": "user", "content": ""},{"role": "assistant", "content": ""}])
else:
conversation.extend([{"role": "user", "content": prompt}, {"role": "assistant", "content": answer}])
choice, contents = mode_load(prompt_files[-1])
if choice == "image":
conversation.append({"role": "user", "image": contents, "content": message['text']})
elif choice == "doc":
format_msg = contents + "\n\n\n" + "{} files uploaded.\n" + message['text']
conversation.append({"role": "user", "content": format_msg})
print(f"Conversation is -\n{conversation}")
input_ids = tokenizer.apply_chat_template(conversation, tokenize=True, add_generation_prompt=True, return_tensors="pt", return_dict=True).to(model.device)
streamer = TextIteratorStreamer(tokenizer, timeout=60.0, skip_prompt=True, skip_special_tokens=True)
generate_kwargs = dict(
max_length=max_length,
streamer=streamer,
do_sample=True,
top_p=top_p,
top_k=top_k,
temperature=temperature,
repetition_penalty=penalty,
eos_token_id=[151329, 151336, 151338],
)
gen_kwargs = {**input_ids, **generate_kwargs}
with torch.no_grad():
thread = Thread(target=model.generate, kwargs=gen_kwargs)
thread.start()
buffer = ""
for new_text in streamer:
buffer += new_text
yield buffer
chatbot = gr.Chatbot(label="Chatbox", height=600, placeholder=DESCRIPTION)
chat_input = gr.MultimodalTextbox(
interactive=True,
placeholder="Enter message or upload a file one time...",
show_label=False,
)
EXAMPLES = [
[{"text": "Describe this image in great detailed.", "files": ["./laptop.jpg"]}],
[{"text": "Please describe this image and guess where it is?", "files": ["./hotel.jpg"]}],
[{"text": "What's in the image, is it real happen?", "files": ["./spacecat.png"]}]
]
with gr.Blocks(css=CSS, theme="soft",fill_height=True) as demo:
gr.HTML(TITLE)
gr.ChatInterface(
fn=stream_chat,
multimodal=True,
textbox=chat_input,
chatbot=chatbot,
fill_height=True,
additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False),
additional_inputs=[
gr.Slider(
minimum=0,
maximum=1,
step=0.1,
value=0.8,
label="Temperature",
render=False,
),
gr.Slider(
minimum=1024,
maximum=8192,
step=1,
value=4096,
label="Max Length",
render=False,
),
gr.Slider(
minimum=0.0,
maximum=1.0,
step=0.1,
value=1.0,
label="top_p",
render=False,
),
gr.Slider(
minimum=1,
maximum=20,
step=1,
value=10,
label="top_k",
render=False,
),
gr.Slider(
minimum=0.0,
maximum=2.0,
step=0.1,
value=1.0,
label="Repetition penalty",
render=False,
),
],
),
gr.Examples(EXAMPLES,[chat_input])
if __name__ == "__main__":
demo.queue(api_open=False).launch(show_api=False, share=False)