import gradio as gr
import requests
import json
import base64
from PIL import Image
import io
import time
def encode_image(image):
if isinstance(image, dict) and 'path' in image:
image_path = image['path']
elif isinstance(image, str):
image_path = image
else:
raise ValueError("Unsupported image format")
with open(image_path, "rb") as image_file:
return base64.b64encode(image_file.read()).decode('utf-8')
def bot_streaming(message, history, api_key, model, system_prompt, temperature, max_tokens, top_p, top_k, frequency_penalty, presence_penalty, repetition_penalty, stop, min_p, top_a, seed, logit_bias, logprobs, top_logprobs, response_format, tools, tool_choice):
headers = {
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json"
}
messages = []
images = []
if system_prompt:
messages.append({"role": "system", "content": system_prompt})
for i, msg in enumerate(history):
if isinstance(msg[0], tuple):
image, text = msg[0]
base64_image = encode_image(image)
messages.append({
"role": "user",
"content": [
{"type": "text", "text": text},
{"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"}}
]
})
messages.append({"role": "assistant", "content": msg[1]})
images.append(Image.open(image['path'] if isinstance(image, dict) else image).convert("RGB"))
else:
messages.append({"role": "user", "content": msg[0]})
messages.append({"role": "assistant", "content": msg[1]})
if isinstance(message, dict) and "files" in message and message["files"]:
image = message["files"][0]
base64_image = encode_image(image)
content = [
{"type": "text", "text": message["text"]},
{"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"}}
]
images.append(Image.open(image['path'] if isinstance(image, dict) else image).convert("RGB"))
else:
content = message["text"] if isinstance(message, dict) else message
messages.append({"role": "user", "content": content})
data = {
"model": model,
"messages": messages,
"stream": True,
"temperature": temperature,
"max_tokens": max_tokens,
"top_p": top_p,
"top_k": top_k,
"frequency_penalty": frequency_penalty,
"presence_penalty": presence_penalty,
"repetition_penalty": repetition_penalty,
"stop": stop if stop else None,
"min_p": min_p,
"top_a": top_a,
"seed": seed,
"logit_bias": logit_bias,
"logprobs": logprobs,
"top_logprobs": top_logprobs,
"response_format": response_format,
"tools": tools,
"tool_choice": tool_choice
}
response = requests.post(
"https://openrouter.ai/api/v1/chat/completions",
headers=headers,
json=data,
stream=True
)
buffer = ""
for chunk in response.iter_lines():
if chunk:
chunk = chunk.decode('utf-8')
if chunk.startswith("data: "):
chunk = chunk[6:]
if chunk.strip() == "[DONE]":
break
try:
chunk_data = json.loads(chunk)
if 'choices' in chunk_data and len(chunk_data['choices']) > 0:
delta = chunk_data['choices'][0].get('delta', {})
if 'content' in delta:
buffer += delta['content']
yield buffer
time.sleep(0.01)
except json.JSONDecodeError:
continue
with gr.Blocks(theme=gr.themes.Soft()) as demo:
gr.Markdown("""
# đ¤ OpenRouter API Multimodal Chat
Chat with various AI models using the OpenRouter API. Supports text and image interactions.
## đ Quick Start:
1. Enter your OpenRouter API key
2. Choose a model or enter a custom model name/endpoint
3. Start chatting!
Enjoy your AI-powered conversation!
""")
with gr.Row():
with gr.Column(scale=1):
api_key = gr.Textbox(label="API Key", type="password", placeholder="Enter your OpenRouter API key")
model = gr.Dropdown(
label="Select Model",
choices=[
"google/gemini-flash-1.5",
"openai/gpt-4o-mini",
"anthropic/claude-3.5-sonnet:beta",
"gryphe/mythomax-l2-13b",
"meta-llama/llama-3.1-70b-instruct",
"microsoft/wizardlm-2-8x22b",
"nousresearch/hermes-3-llama-3.1-405b",
"mistralai/mistral-nemo",
"meta-llama/llama-3.1-8b-instruct",
"deepseek/deepseek-chat",
"mistralai/mistral-tiny",
"openai/gpt-4o",
"mistralai/mistral-7b-instruct",
"meta-llama/llama-3-70b-instruct",
"microsoft/wizardlm-2-7b"
],
value="google/gemini-flash-1.5",
allow_custom_value=True
)
system_prompt = gr.Textbox(label="System Prompt", placeholder="Enter a system prompt (optional)")
with gr.Accordion("Common Settings", open=False):
temperature = gr.Slider(minimum=0, maximum=2, value=1, step=0.1, label="Temperature")
max_tokens = gr.Slider(minimum=1, maximum=4096, value=1000, step=1, label="Max Tokens")
top_p = gr.Slider(minimum=0, maximum=1, value=1, step=0.01, label="Top P")
frequency_penalty = gr.Slider(minimum=-2, maximum=2, value=0, step=0.1, label="Frequency Penalty")
with gr.Accordion("Advanced Settings", open=False):
presence_penalty = gr.Slider(minimum=-2, maximum=2, value=0, step=0.1, label="Presence Penalty")
stop = gr.Textbox(label="Stop Sequence")
top_k = gr.Slider(minimum=0, maximum=100, value=0, step=1, label="Top K")
repetition_penalty = gr.Slider(minimum=0, maximum=2, value=1, step=0.1, label="Repetition Penalty")
min_p = gr.Slider(minimum=0, maximum=1, value=0, step=0.01, label="Min P")
with gr.Accordion("Expert Settings", open=False):
top_a = gr.Slider(minimum=0, maximum=1, value=0, step=0.01, label="Top A")
seed = gr.Number(label="Seed", precision=0)
logit_bias = gr.Textbox(label="Logit Bias (JSON)")
logprobs = gr.Checkbox(label="Log Probabilities")
top_logprobs = gr.Slider(minimum=0, maximum=20, value=0, step=1, label="Top Log Probabilities")
response_format = gr.Textbox(label="Response Format (JSON)")
tools = gr.Textbox(label="Tools (JSON Array)")
tool_choice = gr.Textbox(label="Tool Choice")
with gr.Column(scale=2):
chatbot = gr.ChatInterface(
fn=bot_streaming,
additional_inputs=[
api_key, model, system_prompt, temperature, max_tokens, top_p, top_k,
frequency_penalty, presence_penalty, repetition_penalty, stop,
min_p, top_a, seed, logit_bias, logprobs, top_logprobs,
response_format, tools, tool_choice
],
title="đŦ Chat with AI",
description="Upload images or type your message to start the conversation.",
retry_btn="đ Retry",
undo_btn="âŠī¸ Undo",
clear_btn="đī¸ Clear",
multimodal=True,
cache_examples=False,
fill_height=True,
)
gr.Markdown("""
## đ§ Settings:
- Adjust basic parameters in the "Common Settings" section
- Fine-tune options in the "Advanced Settings" section
- Access expert-level controls in the "Expert Settings" section
- Upload images for multimodal interactions
""")
demo.launch(debug=True, share=True)