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| 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(""" | |
| # 🤖 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 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="💬 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, | |
| ) | |
| gr.Markdown(""" | |
| ## 🔧 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) |