Spaces:
Runtime error
Runtime error
File size: 8,637 Bytes
f326ee0 9114a0c e788115 9114a0c e788115 9114a0c f326ee0 9114a0c e788115 9114a0c e788115 9114a0c e788115 9114a0c e788115 9114a0c f326ee0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 |
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, 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 = []
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("""
# 🤖 OpenRouter API Multimodal Chat
Chat with various AI models using the OpenRouter API. Supports text and image interactions.
## 🚀 Quick Start:
1. Enter your OpenRouter API key
2. Choose a model
3. Start chatting!
## 🔧 Advanced:
- Adjust parameters in the "Advanced Settings" section
- Upload images for multimodal interactions
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"
)
with gr.Accordion("Advanced Settings", open=False):
with gr.Row():
with gr.Column(scale=1):
gr.Markdown("### Basic Parameters")
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")
top_k = gr.Slider(minimum=0, maximum=100, value=0, step=1, label="Top K")
with gr.Column(scale=1):
gr.Markdown("### Penalty Parameters")
frequency_penalty = gr.Slider(minimum=-2, maximum=2, value=0, step=0.1, label="Frequency Penalty")
presence_penalty = gr.Slider(minimum=-2, maximum=2, value=0, step=0.1, label="Presence Penalty")
repetition_penalty = gr.Slider(minimum=0, maximum=2, value=1, step=0.1, label="Repetition Penalty")
with gr.Row():
with gr.Column(scale=1):
gr.Markdown("### Advanced Control")
stop = gr.Textbox(label="Stop Sequence")
min_p = gr.Slider(minimum=0, maximum=1, value=0, step=0.01, label="Min P")
top_a = gr.Slider(minimum=0, maximum=1, value=0, step=0.01, label="Top A")
seed = gr.Number(label="Seed", precision=0)
with gr.Column(scale=1):
gr.Markdown("### Logging and Formatting")
logprobs = gr.Checkbox(label="Log Probabilities")
top_logprobs = gr.Slider(minimum=0, maximum=20, value=0, step=1, label="Top Log Probabilities")
logit_bias = gr.Textbox(label="Logit Bias (JSON)")
response_format = gr.Textbox(label="Response Format (JSON)")
with gr.Row():
with gr.Column(scale=1):
gr.Markdown("### Tools")
tools = gr.Textbox(label="Tools (JSON Array)", lines=3)
tool_choice = gr.Textbox(label="Tool Choice")
with gr.Column(scale=2):
chatbot = gr.ChatInterface(
fn=bot_streaming,
additional_inputs=[
api_key, model, 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="💬 Chat with AI",
description="Upload images or type your message to start the conversation.",
retry_btn="🔄 Retry",
undo_btn="↩️ Undo",
clear_btn="🗑️ Clear",
multimodal=True,
cache_examples=False,
fill_height=True,
)
demo.launch(debug=True, share=True) |