Create app.py

app.py

@@ -0,0 +1,196 @@
+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.Group():
+                    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.Group():
+                    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.Group():
+                    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.Group():
+                    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, 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)