import gradio as gr
import google.generativeai as genai
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, harassment, hate_speech, sexually_explicit, dangerous_content):
    genai.configure(api_key=api_key)
    
    messages = []
    images = []

    if system_prompt:
        messages.append({"role": "system", "content": system_prompt})

    for i, msg in enumerate(history):
        if isinstance(msg[0], tuple):
            # This is a message with an image
            image, text = msg[0]
            base64_image = encode_image(image)
            messages.append({
                "role": "user",
                "parts": [
                    {"text": text},
                    {"inline_data": {"mime_type": "image/jpeg", "data": base64_image}}
                ]
            })
            images.append(Image.open(image['path'] if isinstance(image, dict) else image).convert("RGB"))
        else:
            # This is a text-only message
            messages.append({"role": "user", "parts": [{"text": str(msg[0])}]})
        
        # Add the model's response
        messages.append({"role": "model", "parts": [{"text": str(msg[1])}]})

    # Handle the current message
    if isinstance(message, dict) and "files" in message and message["files"]:
        # This is a message with an image
        image = message["files"][0]
        base64_image = encode_image(image)
        content = [
            {"text": message["text"]},
            {"inline_data": {"mime_type": "image/jpeg", "data": base64_image}}
        ]
        images.append(Image.open(image['path'] if isinstance(image, dict) else image).convert("RGB"))
    else:
        # This is a text-only message
        content = [{"text": message["text"] if isinstance(message, dict) else str(message)}]

    messages.append({"role": "user", "parts": content})

    model = genai.GenerativeModel(model_name=model)
    
    safety_settings = [
        {"category": genai.types.HarmCategory.HARM_CATEGORY_HARASSMENT, "threshold": getattr(genai.types.HarmBlockThreshold, harassment)},
        {"category": genai.types.HarmCategory.HARM_CATEGORY_HATE_SPEECH, "threshold": getattr(genai.types.HarmBlockThreshold, hate_speech)},
        {"category": genai.types.HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT, "threshold": getattr(genai.types.HarmBlockThreshold, sexually_explicit)},
        {"category": genai.types.HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT, "threshold": getattr(genai.types.HarmBlockThreshold, dangerous_content)}
    ]
    
    chat = model.start_chat(history=messages)
    
    response = chat.send_message(
        content,
        stream=True,
        generation_config=genai.types.GenerationConfig(
            temperature=temperature,
            max_output_tokens=max_tokens,
            top_p=top_p,
            top_k=top_k
        ),
        safety_settings=safety_settings
    )

    buffer = ""
    for chunk in response:
        if hasattr(chunk, 'text') and chunk.text:
            buffer += chunk.text
            yield buffer
            time.sleep(0.01)
        if hasattr(chunk, 'finish_reason') and chunk.finish_reason:
            break

    if buffer:
        yield buffer


with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown("""
    # 🤖 Google Gemini API Multimodal Chat

    Chat with Google Gemini AI models. Supports text and image interactions.

    ## 🚀 Quick Start:
    1. Enter your Google AI API key
    2. Choose a model
    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 Google AI API key")
            model = gr.Dropdown(
                label="Select Model",
                choices=[
                    "gemini-1.5-pro",
                    "gemini-1.5-pro-001",
                    "gemini-1.5-pro-vision-latest",
                    "gemini-1.5-pro-latest",
                    "gemini-1.5-flash",
                    "gemini-1.5-flash-002",
                    "gemini-1.0-pro",
                    "gemini-1.0-pro-001",
                    "gemini-1.0-pro-vision-latest",
                    "gemini-1.0-pro-latest"
                ],
                value="gemini-1.5-pro",
            )
            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=1, value=0.7, step=0.1, label="Temperature")
                max_tokens = gr.Slider(minimum=1, maximum=2048, value=1000, step=1, label="Max Tokens")
                top_p = gr.Slider(minimum=0, maximum=1, value=0.95, step=0.01, label="Top P")
                top_k = gr.Slider(minimum=1, maximum=40, value=40, step=1, label="Top K")
            
            with gr.Accordion("Safety Settings", open=False):
                harassment = gr.Dropdown(label="Harassment", choices=["BLOCK_NONE", "BLOCK_ONLY_HIGH", "BLOCK_MEDIUM_AND_ABOVE", "BLOCK_LOW_AND_ABOVE"], value="BLOCK_MEDIUM_AND_ABOVE")
                hate_speech = gr.Dropdown(label="Hate Speech", choices=["BLOCK_NONE", "BLOCK_ONLY_HIGH", "BLOCK_MEDIUM_AND_ABOVE", "BLOCK_LOW_AND_ABOVE"], value="BLOCK_MEDIUM_AND_ABOVE")
                sexually_explicit = gr.Dropdown(label="Sexually Explicit", choices=["BLOCK_NONE", "BLOCK_ONLY_HIGH", "BLOCK_MEDIUM_AND_ABOVE", "BLOCK_LOW_AND_ABOVE"], value="BLOCK_MEDIUM_AND_ABOVE")
                dangerous_content = gr.Dropdown(label="Dangerous Content", choices=["BLOCK_NONE", "BLOCK_ONLY_HIGH", "BLOCK_MEDIUM_AND_ABOVE", "BLOCK_LOW_AND_ABOVE"], value="BLOCK_MEDIUM_AND_ABOVE")

        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,
                    harassment, hate_speech, sexually_explicit, dangerous_content
                ],
                title="💬 Chat with Google Gemini 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 safety options in the "Safety Settings" section
    - Upload images for multimodal interactions
    """)

demo.launch(debug=True, share=True)