app.py

import os
import base64
import gradio as gr
import requests
import json
from io import BytesIO
from PIL import Image
import time

# Get API key from environment variable for security
OPENROUTER_API_KEY = os.environ.get("OPENROUTER_API_KEY", "")

# Model information
free_models = [
    ("Google: Gemini Pro 2.0 Experimental (free)", "google/gemini-2.0-pro-exp-02-05:free", 0, 0, 2000000),
    ("Google: Gemini 2.0 Flash Thinking Experimental 01-21 (free)", "google/gemini-2.0-flash-thinking-exp:free", 0, 0, 1048576),
    ("Google: Gemini Flash 2.0 Experimental (free)", "google/gemini-2.0-flash-exp:free", 0, 0, 1048576),
    ("Google: Gemini Pro 2.5 Experimental (free)", "google/gemini-2.5-pro-exp-03-25:free", 0, 0, 1000000),
    ("Google: Gemini Flash 1.5 8B Experimental", "google/gemini-flash-1.5-8b-exp", 0, 0, 1000000),
    ("DeepSeek: DeepSeek R1 Zero (free)", "deepseek/deepseek-r1-zero:free", 0, 0, 163840),
    ("DeepSeek: R1 (free)", "deepseek/deepseek-r1:free", 0, 0, 163840),
    ("DeepSeek: DeepSeek V3 Base (free)", "deepseek/deepseek-v3-base:free", 0, 0, 131072),
    ("DeepSeek: DeepSeek V3 0324 (free)", "deepseek/deepseek-chat-v3-0324:free", 0, 0, 131072),
    ("Google: Gemma 3 4B (free)", "google/gemma-3-4b-it:free", 0, 0, 131072),
    ("Google: Gemma 3 12B (free)", "google/gemma-3-12b-it:free", 0, 0, 131072),
    ("Nous: DeepHermes 3 Llama 3 8B Preview (free)", "nousresearch/deephermes-3-llama-3-8b-preview:free", 0, 0, 131072),
    ("Qwen: Qwen2.5 VL 72B Instruct (free)", "qwen/qwen2.5-vl-72b-instruct:free", 0, 0, 131072),
    ("DeepSeek: DeepSeek V3 (free)", "deepseek/deepseek-chat:free", 0, 0, 131072),
    ("NVIDIA: Llama 3.1 Nemotron 70B Instruct (free)", "nvidia/llama-3.1-nemotron-70b-instruct:free", 0, 0, 131072),
    ("Meta: Llama 3.2 1B Instruct (free)", "meta-llama/llama-3.2-1b-instruct:free", 0, 0, 131072),
    ("Meta: Llama 3.2 11B Vision Instruct (free)", "meta-llama/llama-3.2-11b-vision-instruct:free", 0, 0, 131072),
    ("Meta: Llama 3.1 8B Instruct (free)", "meta-llama/llama-3.1-8b-instruct:free", 0, 0, 131072),
    ("Mistral: Mistral Nemo (free)", "mistralai/mistral-nemo:free", 0, 0, 128000),
    ("Mistral: Mistral Small 3.1 24B (free)", "mistralai/mistral-small-3.1-24b-instruct:free", 0, 0, 96000),
    ("Google: Gemma 3 27B (free)", "google/gemma-3-27b-it:free", 0, 0, 96000),
    ("Qwen: Qwen2.5 VL 3B Instruct (free)", "qwen/qwen2.5-vl-3b-instruct:free", 0, 0, 64000),
    ("DeepSeek: R1 Distill Qwen 14B (free)", "deepseek/deepseek-r1-distill-qwen-14b:free", 0, 0, 64000),
    ("Qwen: Qwen2.5-VL 7B Instruct (free)", "qwen/qwen-2.5-vl-7b-instruct:free", 0, 0, 64000),
    ("Google: LearnLM 1.5 Pro Experimental (free)", "google/learnlm-1.5-pro-experimental:free", 0, 0, 40960),
    ("Qwen: QwQ 32B (free)", "qwen/qwq-32b:free", 0, 0, 40000),
    ("Google: Gemini 2.0 Flash Thinking Experimental (free)", "google/gemini-2.0-flash-thinking-exp-1219:free", 0, 0, 40000),
    ("Bytedance: UI-TARS 72B (free)", "bytedance-research/ui-tars-72b:free", 0, 0, 32768),
    ("Qwerky 72b (free)", "featherless/qwerky-72b:free", 0, 0, 32768),
    ("OlympicCoder 7B (free)", "open-r1/olympiccoder-7b:free", 0, 0, 32768),
    ("OlympicCoder 32B (free)", "open-r1/olympiccoder-32b:free", 0, 0, 32768),
    ("Google: Gemma 3 1B (free)", "google/gemma-3-1b-it:free", 0, 0, 32768),
    ("Reka: Flash 3 (free)", "rekaai/reka-flash-3:free", 0, 0, 32768),
    ("Dolphin3.0 R1 Mistral 24B (free)", "cognitivecomputations/dolphin3.0-r1-mistral-24b:free", 0, 0, 32768),
    ("Dolphin3.0 Mistral 24B (free)", "cognitivecomputations/dolphin3.0-mistral-24b:free", 0, 0, 32768),
    ("Mistral: Mistral Small 3 (free)", "mistralai/mistral-small-24b-instruct-2501:free", 0, 0, 32768),
    ("Qwen2.5 Coder 32B Instruct (free)", "qwen/qwen-2.5-coder-32b-instruct:free", 0, 0, 32768),
    ("Qwen2.5 72B Instruct (free)", "qwen/qwen-2.5-72b-instruct:free", 0, 0, 32768),
    ("Meta: Llama 3.2 3B Instruct (free)", "meta-llama/llama-3.2-3b-instruct:free", 0, 0, 20000),
    ("Qwen: QwQ 32B Preview (free)", "qwen/qwq-32b-preview:free", 0, 0, 16384),
    ("DeepSeek: R1 Distill Qwen 32B (free)", "deepseek/deepseek-r1-distill-qwen-32b:free", 0, 0, 16000),
    ("Qwen: Qwen2.5 VL 32B Instruct (free)", "qwen/qwen2.5-vl-32b-instruct:free", 0, 0, 8192),
    ("Moonshot AI: Moonlight 16B A3B Instruct (free)", "moonshotai/moonlight-16b-a3b-instruct:free", 0, 0, 8192),
    ("DeepSeek: R1 Distill Llama 70B (free)", "deepseek/deepseek-r1-distill-llama-70b:free", 0, 0, 8192),
    ("Qwen 2 7B Instruct (free)", "qwen/qwen-2-7b-instruct:free", 0, 0, 8192),
    ("Google: Gemma 2 9B (free)", "google/gemma-2-9b-it:free", 0, 0, 8192),
    ("Mistral: Mistral 7B Instruct (free)", "mistralai/mistral-7b-instruct:free", 0, 0, 8192),
    ("Microsoft: Phi-3 Mini 128K Instruct (free)", "microsoft/phi-3-mini-128k-instruct:free", 0, 0, 8192),
    ("Microsoft: Phi-3 Medium 128K Instruct (free)", "microsoft/phi-3-medium-128k-instruct:free", 0, 0, 8192),
    ("Meta: Llama 3 8B Instruct (free)", "meta-llama/llama-3-8b-instruct:free", 0, 0, 8192),
    ("OpenChat 3.5 7B (free)", "openchat/openchat-7b:free", 0, 0, 8192),
    ("Meta: Llama 3.3 70B Instruct (free)", "meta-llama/llama-3.3-70b-instruct:free", 0, 0, 8000),
    ("AllenAI: Molmo 7B D (free)", "allenai/molmo-7b-d:free", 0, 0, 4096),
    ("Rogue Rose 103B v0.2 (free)", "sophosympatheia/rogue-rose-103b-v0.2:free", 0, 0, 4096),
    ("Toppy M 7B (free)", "undi95/toppy-m-7b:free", 0, 0, 4096),
    ("Hugging Face: Zephyr 7B (free)", "huggingfaceh4/zephyr-7b-beta:free", 0, 0, 4096),
    ("MythoMax 13B (free)", "gryphe/mythomax-l2-13b:free", 0, 0, 4096),
]

# Filter for vision models
vision_model_ids = [
    "meta-llama/llama-3.2-11b-vision-instruct:free",
    "qwen/qwen2.5-vl-72b-instruct:free", 
    "qwen/qwen2.5-vl-3b-instruct:free",
    "qwen/qwen2.5-vl-32b-instruct:free",
    "qwen/qwen-2.5-vl-7b-instruct:free",
    "google/gemini-2.0-pro-exp-02-05:free",
    "google/gemini-2.5-pro-exp-03-25:free"
]

# Format model names to include context size
def format_model_name(name, context_size):
    if context_size >= 1000000:
        context_str = f"{context_size/1000000:.1f}M tokens"
    else:
        context_str = f"{context_size/1000:.0f}K tokens"
    return f"{name} ({context_str})"

# Prefilter vision models
vision_models = [(format_model_name(name, context_size), model_id, context_size) 
                for name, model_id, _, _, context_size in free_models 
                if model_id in vision_model_ids]

text_models = [(format_model_name(name, context_size), model_id, context_size) 
              for name, model_id, _, _, context_size in free_models]

def encode_image(image):
    """Convert PIL Image to base64 string"""
    buffered = BytesIO()
    image.save(buffered, format="JPEG")
    return base64.b64encode(buffered.getvalue()).decode("utf-8")

def encode_file(file_path):
    """Convert text file to string"""
    try:
        with open(file_path, 'r', encoding='utf-8') as file:
            return file.read()
    except Exception as e:
        return f"Error reading file: {str(e)}"

def process_message_stream(message, chat_history, model_name, uploaded_image=None, uploaded_file=None, 
                           temperature=0.7, top_p=1.0, max_tokens=None, stream=True):
    """Process message and stream the model response"""
    # Extract model_id from the display name
    model_id = model_name.split(' ')[1] if len(model_name.split(' ')) > 1 else model_name
    
    # Check if API key is set
    if not OPENROUTER_API_KEY:
        yield "Please set your OpenRouter API key in the environment variables.", chat_history
        return
    
    # Setup headers and URL
    headers = {
        "Content-Type": "application/json",
        "Authorization": f"Bearer {OPENROUTER_API_KEY}",
        "HTTP-Referer": "https://huggingface.co/spaces",  # Replace with your actual space URL in production
    }
    
    url = "https://openrouter.ai/api/v1/chat/completions"
    
    # Build message content
    messages = []
    
    # Add chat history
    for item in chat_history:
        if isinstance(item, tuple):
            # Old format compatibility
            human_msg, ai_msg = item
            messages.append({"role": "user", "content": human_msg})
            messages.append({"role": "assistant", "content": ai_msg})
        else:
            # New message format
            messages.append(item)
    
    # Add current message with any attachments
    if uploaded_image:
        # Image processing for vision models
        base64_image = encode_image(uploaded_image)
        content = [
            {"type": "text", "text": message}
        ]
        
        # Add text from file if provided
        if uploaded_file:
            file_content = encode_file(uploaded_file)
            content[0]["text"] = f"{message}\n\nFile content:\n```\n{file_content}\n```"
            
        # Add image
        content.append({
            "type": "image_url",
            "image_url": {
                "url": f"data:image/jpeg;base64,{base64_image}"
            }
        })
        
        messages.append({"role": "user", "content": content})
    else:
        if uploaded_file:
            file_content = encode_file(uploaded_file)
            content = f"{message}\n\nFile content:\n```\n{file_content}\n```"
            messages.append({"role": "user", "content": content})
        else:
            messages.append({"role": "user", "content": message})
    
    # Get context length for the model
    context_length = next((context for _, model_id, context in text_models if model_id == model_id), 4096)
    
    # Calculate default max tokens if not specified
    if not max_tokens:
        # Use 25% of context length as a reasonable default
        max_tokens = min(4000, int(context_length * 0.25))
    
    # Build request data
    data = {
        "model": model_id,
        "messages": messages,
        "stream": stream,
        "temperature": temperature,
        "top_p": top_p,
        "max_tokens": max_tokens
    }
    
    try:
        # Create a new message pair in the chat history
        user_msg = {"role": "user", "content": message}
        ai_msg = {"role": "assistant", "content": ""}
        chat_history.append(user_msg)
        chat_history.append(ai_msg)
        
        full_response = ""
        
        if stream:
            # Make streaming API call
            with requests.post(url, headers=headers, json=data, stream=True) as response:
                response.raise_for_status()
                buffer = ""
                
                for chunk in response.iter_content(chunk_size=1024, decode_unicode=False):
                    if chunk:
                        buffer += chunk.decode('utf-8')
                        
                        while True:
                            line_end = buffer.find('\n')
                            if line_end == -1:
                                break
                                
                            line = buffer[:line_end].strip()
                            buffer = buffer[line_end + 1:]
                            
                            if line.startswith('data: '):
                                data = line[6:]
                                if data == '[DONE]':
                                    break
                                    
                                try:
                                    data_obj = json.loads(data)
                                    delta_content = data_obj["choices"][0]["delta"].get("content", "")
                                    if delta_content:
                                        full_response += delta_content
                                        # Update the last assistant message
                                        chat_history[-1]["content"] = full_response
                                        yield chat_history
                                except json.JSONDecodeError:
                                    pass
        else:
            # Non-streaming API call
            response = requests.post(url, headers=headers, json=data)
            response.raise_for_status()
            result = response.json()
            full_response = result.get("choices", [{}])[0].get("message", {}).get("content", "No response")
            chat_history[-1]["content"] = full_response
            yield chat_history
        
        return chat_history
    
    except Exception as e:
        error_msg = f"Error: {str(e)}"
        chat_history[-1]["content"] = error_msg
        yield chat_history

# Create a nice CSS theme
css = """
.gradio-container {
    font-family: 'Inter', -apple-system, BlinkMacSystemFont, sans-serif;
}
.chat-message {
    padding: 15px;
    border-radius: 10px;
    margin-bottom: 10px;
}
.user-message {
    background-color: #f0f4f8;
}
.assistant-message {
    background-color: #e9f5ff;
}
#chat-container {
    height: 600px;
    overflow-y: auto;
}
#chat-input {
    min-height: 120px;
    border-radius: 8px;
    padding: 10px;
}
#model-select-container {
    border-radius: 8px;
    padding: 15px;
    background-color: #f8fafc;
}
.app-header {
    text-align: center;
    margin-bottom: 20px;
}
.app-header h1 {
    font-weight: 700;
    color: #2C3E50;
    margin-bottom: 5px;
}
.app-header p {
    color: #7F8C8D;
    margin-top: 0;
}
.parameter-container {
    background-color: #f8fafc;
    padding: 10px;
    border-radius: 8px;
    margin-top: 10px;
}
.file-upload-container {
    margin-top: 10px;
}
"""

with gr.Blocks(css=css, theme=gr.themes.Soft()) as demo:
    gr.HTML("""
    <div class="app-header">
        <h1>🔆 CrispChat</h1>
        <p>Chat with free OpenRouter AI models - supports text, images, and files</p>
    </div>
    """)
    
    with gr.Row():
        with gr.Column(scale=4):
            chatbot = gr.Chatbot(
                height=600,
                show_copy_button=True,
                show_share_button=False,
                elem_id="chatbot",
                layout="bubble",
                avatar_images=("👤", "🤖"),
                bubble_full_width=False,
                type="messages"  # Use new message format
            )
            
            with gr.Row():
                with gr.Column(scale=10):
                    user_message = gr.Textbox(
                        placeholder="Type your message here...",
                        show_label=False,
                        elem_id="chat-input",
                        lines=3
                    )
                    
                    with gr.Row():
                        image_upload = gr.Image(
                            type="pil", 
                            label="Image (optional)",
                            show_label=True,
                            scale=1
                        )
                        
                        file_upload = gr.File(
                            label="Text File (optional)",
                            file_types=[".txt", ".md", ".py", ".js", ".html", ".css", ".json"],
                            scale=1
                        )
                        
                        submit_btn = gr.Button("Send", scale=1, variant="primary")
            
        with gr.Column(scale=2):
            with gr.Accordion("Model Selection", open=True):
                using_vision = gr.Checkbox(label="Using image", value=False)
                
                model_selector = gr.Dropdown(
                    choices=[name for name, _, _ in text_models],
                    value=text_models[0][0],
                    label="Select Model",
                    elem_id="model-selector"
                )
                
                context_info = gr.Markdown(value=f"Context: {text_models[0][2]:,} tokens")
                
            with gr.Accordion("Parameters", open=False):
                with gr.Group():
                    temperature = gr.Slider(
                        minimum=0.0, 
                        maximum=2.0, 
                        value=0.7, 
                        step=0.1, 
                        label="Temperature",
                        info="Higher = more creative, Lower = more deterministic"
                    )
                    
                    top_p = gr.Slider(
                        minimum=0.1, 
                        maximum=1.0, 
                        value=1.0, 
                        step=0.1, 
                        label="Top P",
                        info="Controls token diversity"
                    )
                    
                    max_tokens = gr.Slider(
                        minimum=100, 
                        maximum=8000, 
                        value=1000, 
                        step=100, 
                        label="Max Tokens",
                        info="Maximum length of the response"
                    )
                    
                    use_streaming = gr.Checkbox(
                        label="Stream Response", 
                        value=True,
                        info="Show response as it's generated"
                    )
            
            with gr.Accordion("Tips", open=False):
                gr.Markdown("""
                * Select a vision-capable model for images
                * Upload text files to include their content
                * Check model context window sizes
                * Adjust temperature for creativity level
                * Top P controls diversity of responses
                """)
    
    # Define events
    def update_model_selector(use_vision):
        if use_vision:
            return (
                gr.Dropdown(choices=[name for name, _, _ in vision_models], value=vision_models[0][0]),
                f"Context: {vision_models[0][2]:,} tokens"
            )
        else:
            return (
                gr.Dropdown(choices=[name for name, _, _ in text_models], value=text_models[0][0]),
                f"Context: {text_models[0][2]:,} tokens"
            )
    
    def update_context_info(model_name):
        # Extract context size from model name
        for name, _, context_size in text_models:
            if name == model_name:
                return f"Context: {context_size:,} tokens"
        for name, _, context_size in vision_models:
            if name == model_name:
                return f"Context: {context_size:,} tokens"
        return "Context size unknown"
    
    using_vision.change(
        fn=update_model_selector,
        inputs=using_vision,
        outputs=[model_selector, context_info]
    )
    
    model_selector.change(
        fn=update_context_info,
        inputs=model_selector,
        outputs=context_info
    )
    
    # Submit function
    def on_submit(message, history, model, image, file, temp, top_p_val, max_tok, stream):
        if not message and not image and not file:
            return "", history
        return "", process_message_stream(
            message, 
            history, 
            model, 
            image, 
            file.name if file else None,
            temperature=temp,
            top_p=top_p_val,
            max_tokens=max_tok,
            stream=stream
        )
    
    # Set up submission events
    submit_btn.click(
        on_submit,
        inputs=[
            user_message, chatbot, model_selector, 
            image_upload, file_upload,
            temperature, top_p, max_tokens, use_streaming
        ],
        outputs=[user_message, chatbot]
    )
    
    user_message.submit(
        on_submit,
        inputs=[
            user_message, chatbot, model_selector, 
            image_upload, file_upload,
            temperature, top_p, max_tokens, use_streaming
        ],
        outputs=[user_message, chatbot]
    )

# Define FastAPI endpoint
from fastapi import FastAPI, Request, HTTPException
from fastapi.responses import JSONResponse
from pydantic import BaseModel
from fastapi.middleware.cors import CORSMiddleware

app = FastAPI()

class GenerateRequest(BaseModel):
    message: str
    model: str = None
    image_data: str = None

@app.post("/api/generate")
async def api_generate(request: GenerateRequest):
    """API endpoint for generating responses"""
    try:
        message = request.message
        model_name = request.model
        image_data = request.image_data
        
        # Process image if provided
        image = None
        if image_data:
            try:
                # Decode base64 image
                image_bytes = base64.b64decode(image_data)
                image = Image.open(BytesIO(image_bytes))
            except Exception as e:
                return JSONResponse(
                    status_code=400,
                    content={"error": f"Image processing error: {str(e)}"}
                )
        
        # Generate response
        try:
            # Setup headers and URL
            headers = {
                "Content-Type": "application/json",
                "Authorization": f"Bearer {OPENROUTER_API_KEY}",
                "HTTP-Referer": "https://huggingface.co/spaces",
            }
            
            url = "https://openrouter.ai/api/v1/chat/completions"
            
            # Get model_id from model_name
            model_id = None
            if model_name:
                for _, mid, _ in text_models + vision_models:
                    if model_name in mid or model_name == mid:
                        model_id = mid
                        break
            
            if not model_id:
                model_id = text_models[0][1]
            
            # Build messages
            messages = []
            
            if image:
                # Image processing for vision models
                base64_image = encode_image(image)
                content = [
                    {"type": "text", "text": message},
                    {
                        "type": "image_url",
                        "image_url": {
                            "url": f"data:image/jpeg;base64,{base64_image}"
                        }
                    }
                ]
                messages.append({"role": "user", "content": content})
            else:
                messages.append({"role": "user", "content": message})
            
            # Build request data
            data = {
                "model": model_id,
                "messages": messages,
                "temperature": 0.7
            }
            
            # Make API call
            response = requests.post(url, headers=headers, json=data)
            response.raise_for_status()
            
            # Parse response
            result = response.json()
            reply = result.get("choices", [{}])[0].get("message", {}).get("content", "No response")
            
            return {"response": reply}
        
        except Exception as e:
            return JSONResponse(
                status_code=500,
                content={"error": f"Error generating response: {str(e)}"}
            )
            
    except Exception as e:
        return JSONResponse(
            status_code=500,
            content={"error": f"Server error: {str(e)}"}
        )

# Add CORS middleware to allow cross-origin requests
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Mount Gradio app
import gradio as gr
app = gr.mount_gradio_app(app, demo, path="/")

# Start the app
if __name__ == "__main__":
    # Use 'uvicorn' directly in HF Spaces
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=7860)