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import gradio as gr
from PIL import Image
import requests
import os
from together import Together
import base64
from threading import Thread
import time
# Initialize Together client
client = Together()
# Ensure API key is set
if "TOGETHER_API_KEY" not in os.environ:
raise ValueError("Please set the TOGETHER_API_KEY environment variable")
def encode_image(image_path):
with open(image_path, "rb") as image_file:
return base64.b64encode(image_file.read()).decode('utf-8')
def bot_streaming(message, history, max_new_tokens=250):
txt = message["text"]
messages = []
images = []
for i, msg in enumerate(history):
if isinstance(msg[0], tuple):
messages.append({"role": "user", "content": [{"type": "text", "text": history[i+1][0]}, {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{encode_image(msg[0][0])}"}}]})
messages.append({"role": "assistant", "content": [{"type": "text", "text": history[i+1][1]}]})
elif isinstance(history[i-1], tuple) and isinstance(msg[0], str):
pass
elif isinstance(history[i-1][0], str) and isinstance(msg[0], str):
messages.append({"role": "user", "content": [{"type": "text", "text": msg[0]}]})
messages.append({"role": "assistant", "content": [{"type": "text", "text": msg[1]}]})
if len(message["files"]) == 1:
if isinstance(message["files"][0], str): # examples
image_path = message["files"][0]
else: # regular input
image_path = message["files"][0]["path"]
messages.append({"role": "user", "content": [{"type": "text", "text": txt}, {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{encode_image(image_path)}"}}]})
else:
messages.append({"role": "user", "content": [{"type": "text", "text": txt}]})
stream = client.chat.completions.create(
model="meta-llama/Llama-Vision-Free",
messages=messages,
max_tokens=max_new_tokens,
stream=True,
)
buffer = ""
for chunk in stream:
if chunk.choices[0].delta.content is not None:
buffer += chunk.choices[0].delta.content
time.sleep(0.01)
yield buffer
demo = gr.ChatInterface(
fn=bot_streaming,
title="Meta Llama 3.2 Vision 11B",
textbox=gr.MultimodalTextbox(),
additional_inputs=[
gr.Slider(
minimum=10,
maximum=500,
value=250,
step=10,
label="Maximum number of new tokens to generate",
)
],
cache_examples=False,
description="Try Multimodal Llama by Meta with the Together API in this demo. Upload an image, and start chatting about it",
stop_btn="Stop Generation",
fill_height=True,
multimodal=True
)
if __name__ == "__main__":
demo.launch(debug=True) |