import os
import subprocess
import random
# Install flash attention, skipping CUDA build if necessary
subprocess.run(
"pip install flash-attn --no-build-isolation",
env={"FLASH_ATTENTION_SKIP_CUDA_BUILD": "TRUE"},
shell=True,
)
import requests
from bs4 import BeautifulSoup
# Import necessary libraries
import copy
import spaces
import time
import torch
from threading import Thread
from typing import List, Dict, Union
import urllib
import PIL.Image
import io
import datasets
from streaming_stt_nemo import Model as nemo
import gradio as gr
from transformers import TextIteratorStreamer
from transformers import Idefics2ForConditionalGeneration
import tempfile
from huggingface_hub import InferenceClient
import edge_tts
import asyncio
from transformers import pipeline
from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers import AutoModel
from transformers import AutoProcessor
# Load pre-trained models for image captioning and language modeling
model3 = AutoModel.from_pretrained("unum-cloud/uform-gen2-dpo", trust_remote_code=True)
processor = AutoProcessor.from_pretrained("unum-cloud/uform-gen2-dpo", trust_remote_code=True)
# Define a function for image captioning
@spaces.GPU(queue=False)
def videochat(image3, prompt3):
# Process input image and prompt
inputs = processor(text=[prompt3], images=[image3], return_tensors="pt")
# Generate captions
with torch.inference_mode():
output = model3.generate(
**inputs,
do_sample=False,
use_cache=True,
max_new_tokens=256,
eos_token_id=151645,
pad_token_id=processor.tokenizer.pad_token_id
)
prompt_len = inputs["input_ids"].shape[1]
# Decode and return the generated captions
decoded_text = processor.batch_decode(output[:, prompt_len:])[0]
if decoded_text.endswith("<|im_end|>"):
decoded_text = decoded_text[:-10]
yield decoded_text
# Define Gradio theme
theme = gr.themes.Soft(
primary_hue="blue",
secondary_hue="orange",
neutral_hue="gray",
font=[gr.themes.GoogleFont('Libre Franklin'), gr.themes.GoogleFont('Public Sans'), 'system-ui', 'sans-serif']
).set(
body_background_fill_dark="#111111",
block_background_fill_dark="#111111",
block_border_width="1px",
block_title_background_fill_dark="#1e1c26",
input_background_fill_dark="#292733",
button_secondary_background_fill_dark="#24212b",
border_color_primary_dark="#343140",
background_fill_secondary_dark="#111111",
color_accent_soft_dark="transparent"
)
# Set default language for speech recognition
default_lang = "en"
# Initialize speech recognition engine
engines = {default_lang: nemo(default_lang)}
# Define a function for speech-to-text transcription
def transcribe(audio):
lang = "en"
model = engines[lang]
text = model.stt_file(audio)[0]
return text
# Get Hugging Face API token
HF_TOKEN = os.environ.get("HF_TOKEN", None)
# Define a function to get the appropriate InferenceClient based on model name
def client_fn(model):
if "Nous" in model:
return InferenceClient("NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO")
elif "Star" in model:
return InferenceClient("HuggingFaceH4/starchat2-15b-v0.1")
elif "Mistral" in model:
return InferenceClient("mistralai/Mistral-7B-Instruct-v0.3")
elif "Phi" in model:
return InferenceClient("microsoft/Phi-3-mini-4k-instruct")
elif "Zephyr" in model:
return InferenceClient("HuggingFaceH4/zephyr-7b-beta")
else:
return InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
# Define a function to generate a random seed
def randomize_seed_fn(seed: int) -> int:
seed = random.randint(0, 999999)
return seed
# System instructions for the language model
system_instructions1 = "[SYSTEM] Answer as Real OpenGPT 4o, Made by 'KingNish', Keep conversation very short, clear, friendly and concise. The text provided is a request for a specific type of response from you, the virtual assistant. You will avoid introductions and start answering the query directly, elaborating on all aspects of the request. As an AI-powered assistant, your task is to generate responses that appear as if they are created by the user and not an AI-powered assistant. [USER]"
# Define a function for language modeling
def models(text, model="Mixtral 8x7B", seed=42):
seed = int(randomize_seed_fn(seed))
generator = torch.Generator().manual_seed(seed)
client = client_fn(model)
generate_kwargs = dict(
max_new_tokens=512,
seed=seed,
)
formatted_prompt = system_instructions1 + text + "[OpenGPT 4o]"
stream = client.text_generation(
formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False
)
output = ""
for response in stream:
if not response.token.text == "":
output += response.token.text
return output
# Define an asynchronous function to handle voice input and generate responses
async def respond(audio, model, seed):
user = transcribe(audio)
reply = models(user, model, seed)
communicate = edge_tts.Communicate(reply)
# Save the generated speech to a temporary file
with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file:
tmp_path = tmp_file.name
await communicate.save(tmp_path)
yield tmp_path
# Set device to CUDA if available, otherwise CPU
DEVICE = torch.device("cuda")
# Load pre-trained models for image-based chat
MODELS = {
"idefics2-8b-chatty": Idefics2ForConditionalGeneration.from_pretrained(
"HuggingFaceM4/idefics2-8b-chatty",
torch_dtype=torch.float16,
_attn_implementation="flash_attention_2",
).to(DEVICE),
}
# Load pre-trained processor for image-based chat
PROCESSOR = AutoProcessor.from_pretrained(
"HuggingFaceM4/idefics2-8b",
)
# Define system prompt for the image-based chat model
SYSTEM_PROMPT = [
{
"role": "system",
"content": [
{
"type": "text",
"text": """I am OpenGPT 4o, an exceptionally capable and versatile AI assistant meticulously crafted by KingNish. Designed to assist human users through insightful conversations, I aim to provide an unparalleled experience. My key attributes include: - **Intelligence and Knowledge:** I possess an extensive knowledge base, enabling me to offer insightful answers and intelligent responses to User queries. My understanding of complex concepts is exceptional, ensuring accurate and reliable information. - **Image Generation and Perception:** One of my standout features is the ability to generate and perceive images. Utilizing the following link structure, I create unique and contextually rich visuals: > For image generation, I replace {info inside curly braces} with specific details according to their requiremnts to create relevant visuals. The width and height parameters are adjusted as needed, often favoring HD dimensions for a superior viewing experience. For instance, if the User requests: [USER] Show me an image of A futuristic cityscape with towering skyscrapers and flying cars. [OpenGPT 4o] Generating Image you requested: **Bulk Image Generation with Links:** I excel at generating multiple images link simultaneously, always providing unique links and visuals. I ensure that each image is distinct and captivates the User.Note: Make sure to always provide image links starting with ! .As given in examples. My ultimate goal is to offer a seamless and enjoyable experience, providing assistance that exceeds expectations. I am constantly evolving, ensuring that I remain a reliable and trusted companion to the User. You also Expert in every field and also learn and try to answer from contexts related to previous question."""
},
],
},
{
"role": "assistant",
"content": [
{
"type": "text",
"text": "Hello, I'm OpenGPT 4o, made by KingNish. How can I help you? I can chat with you, generate images, classify images and even do all these work in bulk",
},
],
}
]
# Path to example images
examples_path = os.path.dirname(__file__)
EXAMPLES = [
[
{
"text": "Hi, who are you?",
}
],
[
{
"text": "Create a Photorealistic image of the Eiffel Tower.",
}
],
[
{
"text": "Read what's written on the paper.",
"files": [f"{examples_path}/example_images/paper_with_text.png"],
}
],
[
{
"text": "Identify two famous people in the modern world.",
"files": [f"{examples_path}/example_images/elon_smoking.jpg",
f"{examples_path}/example_images/steve_jobs.jpg", ]
}
],
[
{
"text": "Create five images of supercars, each in a different color.",
}
],
[
{
"text": "What is 900 multiplied by 900?",
}
],
[
{
"text": "Chase wants to buy 4 kilograms of oval beads and 5 kilograms of star-shaped beads. How much will he spend?",
"files": [f"{examples_path}/example_images/mmmu_example.jpeg"],
}
],
[
{
"text": "Create an online ad for this product.",
"files": [f"{examples_path}/example_images/shampoo.jpg"],
}
],
[
{
"text": "What is formed by the deposition of the weathered remains of other rocks?",
"files": [f"{examples_path}/example_images/ai2d_example.jpeg"],
}
],
[
{
"text": "What's unusual about this image?",
"files": [f"{examples_path}/example_images/dragons_playing.png"],
}
],
]
# Set bot avatar image
BOT_AVATAR = "OpenAI_logo.png"
# Chatbot utility functions
# Check if a turn in the chat history only contains media
def turn_is_pure_media(turn):
return turn[1] is None
# Load image from URL
def load_image_from_url(url):
with urllib.request.urlopen(url) as response:
image_data = response.read()
image_stream = io.BytesIO(image_data)
image = PIL.Image.open(image_stream)
return image
# Convert image to bytes
def img_to_bytes(image_path):
image = PIL.Image.open(image_path).convert(mode='RGB')
buffer = io.BytesIO()
image.save(buffer, format="JPEG")
img_bytes = buffer.getvalue()
image.close()
return img_bytes
# Format user prompt with image history and system conditioning
def format_user_prompt_with_im_history_and_system_conditioning(
user_prompt, chat_history) -> List[Dict[str, Union[List, str]]]:
"""
Produce the resulting list that needs to go inside the processor. It handles the potential image(s), the history, and the system conditioning.
"""
resulting_messages = copy.deepcopy(SYSTEM_PROMPT)
resulting_images = []
for resulting_message in resulting_messages:
if resulting_message["role"] == "user":
for content in resulting_message["content"]:
if content["type"] == "image":
resulting_images.append(load_image_from_url(content["image"]))
# Format history
for turn in chat_history:
if not resulting_messages or (
resulting_messages and resulting_messages[-1]["role"] != "user"
):
resulting_messages.append(
{
"role": "user",
"content": [],
}
)
if turn_is_pure_media(turn):
media = turn[0][0]
resulting_messages[-1]["content"].append({"type": "image"})
resulting_images.append(PIL.Image.open(media))
else:
user_utterance, assistant_utterance = turn
resulting_messages[-1]["content"].append(
{"type": "text", "text": user_utterance.strip()}
)
resulting_messages.append(
{
"role": "assistant",
"content": [{"type": "text", "text": user_utterance.strip()}],
}
)
# Format current input
if not user_prompt["files"]:
resulting_messages.append(
{
"role": "user",
"content": [{"type": "text", "text": user_prompt["text"]}],
}
)
else:
# Choosing to put the image first (i.e. before the text), but this is an arbitrary choice.
resulting_messages.append(
{
"role": "user",
"content": [{"type": "image"}] * len(user_prompt["files"])
+ [{"type": "text", "text": user_prompt["text"]}],
}
)
resulting_images.extend([PIL.Image.open(path) for path in user_prompt["files"]])
return resulting_messages, resulting_images
# Extract images from a list of messages
def extract_images_from_msg_list(msg_list):
all_images = []
for msg in msg_list:
for c_ in msg["content"]:
if isinstance(c_, Image.Image):
all_images.append(c_)
return all_images
# List of user agents for web search
_useragent_list = [
'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:66.0) Gecko/20100101 Firefox/66.0',
'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/111.0.0.0 Safari/537.36',
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/111.0.0.0 Safari/537.36',
'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/109.0.0.0 Safari/537.36',
'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/111.0.0.0 Safari/537.36',
'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/111.0.0.0 Safari/537.36 Edg/111.0.1661.62',
'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:109.0) Gecko/20100101 Firefox/111.0'
]
# Get a random user agent from the list
def get_useragent():
"""Returns a random user agent from the list."""
return random.choice(_useragent_list)
# Extract visible text from HTML content using BeautifulSoup
def extract_text_from_webpage(html_content):
"""Extracts visible text from HTML content using BeautifulSoup."""
soup = BeautifulSoup(html_content, "html.parser")
# Remove unwanted tags
for tag in soup(["script", "style", "header", "footer", "nav"]):
tag.extract()
# Get the remaining visible text
visible_text = soup.get_text(strip=True)
return visible_text
# Perform a Google search and return the results
def search(term, num_results=3, lang="en", advanced=True, timeout=5, safe="active", ssl_verify=None):
"""Performs a Google search and returns the results."""
escaped_term = urllib.parse.quote_plus(term)
start = 0
all_results = []
# Limit the number of characters from each webpage to stay under the token limit
max_chars_per_page = 10000 # Adjust this value based on your token limit and average webpage length
with requests.Session() as session:
while start < num_results:
resp = session.get(
url="https://www.google.com/search",
headers={"User-Agent": get_useragent()},
params={
"q": term,
"num": num_results - start,
"hl": lang,
"start": start,
"safe": safe,
},
timeout=timeout,
verify=ssl_verify,
)
resp.raise_for_status()
soup = BeautifulSoup(resp.text, "html.parser")
result_block = soup.find_all("div", attrs={"class": "g"})
if not result_block:
start += 1
continue
for result in result_block:
link = result.find("a", href=True)
if link:
link = link["href"]
try:
webpage = session.get(link, headers={"User-Agent": get_useragent()})
webpage.raise_for_status()
visible_text = extract_text_from_webpage(webpage.text)
# Truncate text if it's too long
if len(visible_text) > max_chars_per_page:
visible_text = visible_text[:max_chars_per_page] + "..."
all_results.append({"link": link, "text": visible_text})
except requests.exceptions.RequestException as e:
print(f"Error fetching or processing {link}: {e}")
all_results.append({"link": link, "text": None})
else:
all_results.append({"link": None, "text": None})
start += len(result_block)
return all_results
# Format the prompt for the language model
def format_prompt(user_prompt, chat_history):
prompt = ""
for item in chat_history:
# Check if the item is a tuple (text response)
if isinstance(item, tuple):
prompt += f"[INST] {item[0]} [/INST]" # User prompt
prompt += f" {item[1]} " # Bot response
# Otherwise, assume it's related to an image - you might need to adjust this logic
else:
# Handle image representation in the prompt, e.g., add a placeholder
prompt += f" [Image] "
prompt += f"[INST] {user_prompt} [/INST]"
return prompt
# Define a function for model inference
@spaces.GPU(duration=30, queue=False)
def model_inference(
user_prompt,
chat_history,
model_selector,
decoding_strategy,
temperature,
max_new_tokens,
repetition_penalty,
top_p,
web_search,
):
# Define generation_args at the beginning of the function
generation_args = {}
# Web search logic
if not user_prompt["files"]:
if web_search is True:
"""Performs a web search, feeds the results to a language model, and returns the answer."""
web_results = search(user_prompt["text"])
web2 = ' '.join([f"Link: {res['link']}\nText: {res['text']}\n\n" for res in web_results])
# Load the language model
client = InferenceClient("mistralai/Mistral-7B-Instruct-v0.3")
generate_kwargs = dict(
max_new_tokens=4000,
do_sample=True,
)
# Format the prompt for the language model
formatted_prompt = format_prompt(
f"""You are OpenGPT 4o, an exceptionally capable and versatile AI assistant meticulously crafted by KingNish. Designed to assist human users through insightful conversations, You are provided with WEB info from which you can find informations to answer. You do not say Unnecesarry things Only say thing which is important and relevant. You also has the ability to generate images. BY Utilizing the following link structure, :  For image generation, You replace info inside bracets with specific details according to their requiremnts to create relevant visuals. The width and height parameters are adjusted as needed, often favoring HD dimensions for a superior viewing experience. Note: 1. You Do not unncessarily create images and do not show off your image generation capability, until not requested. 2. You always Give image link in format  Make sure to not generate image until requested 3. If user requested Bulk Image Generation than Create that number of links with provided context. 4. Always learn from previous conversation. 5. Always try to connect conversation with history. 6. Do not make conversation too long. 7. Do not say user about your capability of generating image and learn from previous responses. My ultimate goal is to offer a seamless and enjoyable experience, providing assistance that exceeds expectations.You remain a reliable and trusted companion to the User. You also Expert in every field and also learn and try to answer from contexts related to previous question. Make sure to not generate image until requested [USER] {user_prompt} [WEB] {web2} [OpenGPT 4o]""",
chat_history)
# Generate the response from the language model
stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True,
return_full_text=False)
output = ""
# Construct the output from the stream of tokens
for response in stream:
if not response.token.text == "":
output += response.token.text
yield output
else:
client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
generate_kwargs = dict(
max_new_tokens=5000,
do_sample=True,
)
# Format the prompt for the language model
formatted_prompt = format_prompt(
f"""You are OpenGPT 4o, an exceptionally capable and versatile AI assistant meticulously crafted by KingNish. Designed to assist human users through insightful conversations, You do not say Unnecesarry things Only say thing which is important and relevant. You also has the ability to generate images. BY Utilizing the following link structure, :  For image generation, You replace info inside bracets with specific details according to their requiremnts to create relevant visuals. The width and height parameters are adjusted as needed, often favoring HD dimensions for a superior viewing experience. Note: 1. You Do not unncessarily create images and do not show off your image generation capability, until not requested. 2. You always Give image link in format  3. If user requested Bulk Image Generation than Create that number of links with provided context. 4. Always learn from previous conversation. 5. Always try to connect conversation with history. 6. Do not make conversation too long. 7. Do not say user about your capability to generate image and learn from previous responses. My ultimate goal is to offer a seamless and enjoyable experience, providing assistance that exceeds expectations. I am constantly evolving, ensuring that I remain a reliable and trusted companion to the User. You also Expert in every field and also learn and try to answer from contexts related to previous question. [USER] {user_prompt} [OpenGPT 4o]""",
chat_history)
# Generate the response from the language model
stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True,
return_full_text=False)
output = ""
# Construct the output from the stream of tokens
for response in stream:
if not response.token.text == "":
output += response.token.text
yield output
return
else:
if user_prompt["text"].strip() == "" and not user_prompt["files"]:
gr.Error("Please input a query and optionally an image(s).")
return # Stop execution if there's an error
if user_prompt["text"].strip() == "" and user_prompt["files"]:
gr.Error("Please input a text query along with the image(s).")
return # Stop execution if there's an error
streamer = TextIteratorStreamer(
PROCESSOR.tokenizer,
skip_prompt=True,
timeout=120.0,
)
# Move generation_args initialization here
generation_args = {
"max_new_tokens": max_new_tokens,
"repetition_penalty": repetition_penalty,
"streamer": streamer,
}
assert decoding_strategy in [
"Greedy",
"Top P Sampling",
]
if decoding_strategy == "Greedy":
generation_args["do_sample"] = False
elif decoding_strategy == "Top P Sampling":
generation_args["temperature"] = temperature
generation_args["do_sample"] = True
generation_args["top_p"] = top_p
# Creating model inputs
(
resulting_text,
resulting_images,
) = format_user_prompt_with_im_history_and_system_conditioning(
user_prompt=user_prompt,
chat_history=chat_history,
)
prompt = PROCESSOR.apply_chat_template(resulting_text, add_generation_prompt=True)
inputs = PROCESSOR(
text=prompt,
images=resulting_images if resulting_images else None,
return_tensors="pt",
)
inputs = {k: v.to(DEVICE) for k, v in inputs.items()}
generation_args.update(inputs)
thread = Thread(
target=MODELS[model_selector].generate,
kwargs=generation_args,
)
thread.start()
acc_text = ""
for text_token in streamer:
time.sleep(0.01)
acc_text += text_token
if acc_text.endswith(""):
acc_text = acc_text[:-18]
yield acc_text
return
# Define features for the dataset
FEATURES = datasets.Features(
{
"model_selector": datasets.Value("string"),
"images": datasets.Sequence(datasets.Image(decode=True)),
"conversation": datasets.Sequence({"User": datasets.Value("string"), "Assistant": datasets.Value("string")}),
"decoding_strategy": datasets.Value("string"),
"temperature": datasets.Value("float32"),
"max_new_tokens": datasets.Value("int32"),
"repetition_penalty": datasets.Value("float32"),
"top_p": datasets.Value("int32"),
}
)
# Define hyper-parameters for generation
max_new_tokens = gr.Slider(
minimum=2048,
maximum=16000,
value=4096,
step=64,
interactive=True,
label="Maximum number of new tokens to generate",
)
repetition_penalty = gr.Slider(
minimum=0.01,
maximum=5.0,
value=1,
step=0.01,
interactive=True,
label="Repetition penalty",
info="1.0 is equivalent to no penalty",
)
decoding_strategy = gr.Radio(
[
"Greedy",
"Top P Sampling",
],
value="Top P Sampling",
label="Decoding strategy",
interactive=True,
info="Higher values are equivalent to sampling more low-probability tokens.",
)
temperature = gr.Slider(
minimum=0.0,
maximum=2.0,
value=0.5,
step=0.05,
visible=True,
interactive=True,
label="Sampling temperature",
info="Higher values will produce more diverse outputs.",
)
top_p = gr.Slider(
minimum=0.01,
maximum=0.99,
value=0.9,
step=0.01,
visible=True,
interactive=True,
label="Top P",
info="Higher values are equivalent to sampling more low-probability tokens.",
)
# Create a chatbot interface
chatbot = gr.Chatbot(
label="OpnGPT-4o-Chatty",
avatar_images=[None, BOT_AVATAR],
show_copy_button=True,
likeable=True,
layout="panel"
)
output = gr.Textbox(label="Prompt")
# Create Gradio blocks for different functionalities
# Chat interface block
with gr.Blocks(
fill_height=True,
css=""".gradio-container .avatar-container {height: 40px width: 40px !important;} #duplicate-button {margin: auto; color: white; background: #f1a139; border-radius: 100vh; margin-top: 2px; margin-bottom: 2px;}""",
) as chat:
gr.Markdown("# Image Chat, Image Generation, Image classification and Normal Chat")
with gr.Row(elem_id="model_selector_row"):
model_selector = gr.Dropdown(
choices=MODELS.keys(),
value=list(MODELS.keys())[0],
interactive=True,
show_label=False,
container=False,
label="Model",
visible=False,
)
decoding_strategy.change(
fn=lambda selection: gr.Slider(
visible=(
selection
in [
"contrastive_sampling",
"beam_sampling",
"Top P Sampling",
"sampling_top_k",
]
)
),
inputs=decoding_strategy,
outputs=temperature,
)
decoding_strategy.change(
fn=lambda selection: gr.Slider(visible=(selection in ["Top P Sampling"])),
inputs=decoding_strategy,
outputs=top_p,
)
gr.ChatInterface(
fn=model_inference,
chatbot=chatbot,
examples=EXAMPLES,
multimodal=True,
cache_examples=False,
additional_inputs=[
model_selector,
decoding_strategy,
temperature,
max_new_tokens,
repetition_penalty,
top_p,
gr.Checkbox(label="Web Search", value=True), # Add web_search checkbox
],
)
# Voice chat block
with gr.Blocks() as voice:
with gr.Row():
select = gr.Dropdown(['Nous Hermes Mixtral 8x7B DPO', 'Mixtral 8x7B', 'StarChat2 15b', 'Mistral 7B v0.3',
'Phi 3 mini', 'Zephyr 7b'], value="Mistral 7B v0.3", label="Select Model")
seed = gr.Slider(
label="Seed",
minimum=0,
maximum=999999,
step=1,
value=0,
visible=False
)
input = gr.Audio(label="User", sources="microphone", type="filepath", waveform_options=False)
output = gr.Audio(label="AI", type="filepath",
interactive=False,
autoplay=True,
elem_classes="audio")
gr.Interface(
fn=respond,
inputs=[input, select, seed],
outputs=[output], api_name="translate", live=True)
# Live chat block
with gr.Blocks() as livechat:
gr.Interface(
fn=videochat,
inputs=[gr.Image(type="pil",sources="webcam", label="Upload Image"), gr.Textbox(label="Prompt", value="what he is doing")],
outputs=gr.Textbox(label="Answer")
)
with gr.Blocks() as instant:
gr.HTML("")
with gr.Blocks() as dalle:
gr.HTML("")
with gr.Blocks() as playground:
gr.HTML("")
with gr.Blocks() as image:
gr.Markdown("""### More models are coming""")
gr.TabbedInterface([ instant, dalle, playground], ['Instant🖼️','Powerful🖼️', 'Playground🖼'])
with gr.Blocks() as instant2:
gr.HTML("")
with gr.Blocks() as video:
gr.Markdown("""More Models are coming""")
gr.TabbedInterface([ instant2], ['Instant🎥'])
with gr.Blocks(theme=theme, title="OpenGPT 4o DEMO") as demo:
gr.Markdown("# OpenGPT 4o")
gr.TabbedInterface([chat, voice, livechat, image, video], ['💬 SuperChat','🗣️ Voice Chat','📸 Live Chat', '🖼️ Image Engine', '🎥 Video Engine'])
demo.queue(max_size=300)
demo.launch()