import gradio as gr
from groq import Groq
import os
from PIL import Image
import base64
import uuid
import time

API_KEY = "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"
client = Groq(api_key=API_KEY)

# إعداد المجلدات والصور
UPLOAD_FOLDER = "./uploaded_images"
BOT_AVATAR = "https://shfra.netlify.app/imeg.png"
os.makedirs(UPLOAD_FOLDER, exist_ok=True)

# إعدادات الصور
IMAGE_WIDTH = 1111

def preprocess_image(image: Image.Image) -> Image.Image:
    image_height = int(image.height * IMAGE_WIDTH / image.width)
    return image.resize((IMAGE_WIDTH, image_height))

def save_image(image: Image.Image) -> str:
    image_filename = f"{uuid.uuid4()}.jpeg"
    path = os.path.join(UPLOAD_FOLDER, image_filename)
    image.save(path, "JPEG", quality=95)
    return path

def process_image(image_path: str) -> tuple:
    image = Image.open(image_path).convert('RGB')
    image = preprocess_image(image)
    saved_path = save_image(image)
    encoded = base64.b64encode(open(saved_path, 'rb').read()).decode('utf-8')
    return saved_path, encoded

def format_message(content, image_data=None):
    if image_data:
        return [
            {"type": "text", "text": content},
            {"type": "image_url", "image_url": f"data:image/jpeg;base64,{image_data}"}
        ]
    return content

def upload_images(files, uploaded_images):
    new_uploaded = uploaded_images.copy() if uploaded_images else []
    history = []
    for f in files:
        saved, _ = process_image(f)
        new_uploaded.append(saved)
        history.append(("تم رفع صورة:", f"<img src='{saved}' style='max-width:200px;'>"))
    return history, new_uploaded

def chat_with_gpt(user_input, uploaded_images, chat_history):
    chat_history = chat_history or []
    messages = [{"role": "system", "content": "أنت مساعد ذكي تحلل الصور وتجاوب على استفسارات المستخدم بدقة وبأسلوب محترم."}]
    
    if uploaded_images:
        for image_path in uploaded_images:
            _, encoded = process_image(image_path)
            messages.append({"role": "user", "content": format_message("وصف الصورة:", encoded)})

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

    # استدعاء النموذج
    completion = client.chat.completions.create(
        model="llama-3.3-70b-versatile",
        messages=messages,
        temperature=0.7,
        max_tokens=1024,
        top_p=1,
        stream=True
    )
    response = ""
    for chunk in completion:
        if chunk.choices[0].delta.content:
            response += chunk.choices[0].delta.content
            time.sleep(0.01)

    chat_history.append({"role": "user", "content": user_input})
    chat_history.append({"role": "assistant", "content": response})
    return chat_history, uploaded_images

# واجهة Gradio
with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown("# 🤖 ShfraAI - chatBot")
    chatbot = gr.Chatbot(label="ShfraAI", avatar_images=(None, BOT_AVATAR), type="messages")
    user_input = gr.Textbox(placeholder="اكتب رسالتك...", show_label=False)
    upload_button = gr.UploadButton("رفع صور", file_count="multiple")
    submit_button = gr.Button("إرسال")
    uploaded_state = gr.State([])      # لتخزين الصور
    chat_history_state = gr.State([])  # لتخزين المحادثة

    upload_button.upload(
        upload_images,
        inputs=[upload_button, uploaded_state],
        outputs=[chatbot, uploaded_state]
    )

    submit_button.click(
        chat_with_gpt,
        inputs=[user_input, uploaded_state, chat_history_state],
        outputs=[chatbot, chat_history_state]
    )

    user_input.submit(
        chat_with_gpt,
        inputs=[user_input, uploaded_state, chat_history_state],
        outputs=[chatbot, chat_history_state]
    )

if __name__ == "__main__":
    demo.queue().launch(share=True)