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import gradio as gr |
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from groq import Groq |
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import os |
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from PIL import Image |
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import base64 |
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import uuid |
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import time |
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API_KEY = "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" |
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client = Groq(api_key=API_KEY) |
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UPLOAD_FOLDER = "./uploaded_images" |
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BOT_AVATAR = "https://shfra.netlify.app/imeg.png" |
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os.makedirs(UPLOAD_FOLDER, exist_ok=True) |
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IMAGE_WIDTH = 1111 |
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def preprocess_image(image: Image.Image) -> Image.Image: |
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image_height = int(image.height * IMAGE_WIDTH / image.width) |
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return image.resize((IMAGE_WIDTH, image_height)) |
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def save_image(image: Image.Image) -> str: |
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image_filename = f"{uuid.uuid4()}.jpeg" |
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path = os.path.join(UPLOAD_FOLDER, image_filename) |
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image.save(path, "JPEG", quality=95) |
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return path |
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def process_image(image_path: str) -> tuple: |
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image = Image.open(image_path).convert('RGB') |
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image = preprocess_image(image) |
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saved_path = save_image(image) |
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encoded = base64.b64encode(open(saved_path, 'rb').read()).decode('utf-8') |
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return saved_path, encoded |
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def format_message(content, image_data=None): |
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if image_data: |
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return [ |
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{"type": "text", "text": content}, |
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{"type": "image_url", "image_url": f"data:image/jpeg;base64,{image_data}"} |
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] |
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return content |
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def upload_images(files, uploaded_images): |
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new_uploaded = uploaded_images.copy() if uploaded_images else [] |
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history = [] |
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for f in files: |
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saved, _ = process_image(f) |
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new_uploaded.append(saved) |
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history.append(("تم رفع صورة:", f"<img src='{saved}' style='max-width:200px;'>")) |
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return history, new_uploaded |
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def chat_with_gpt(user_input, uploaded_images, chat_history): |
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chat_history = chat_history or [] |
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messages = [{"role": "system", "content": "أنت مساعد ذكي تحلل الصور وتجاوب على استفسارات المستخدم بدقة وبأسلوب محترم."}] |
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if uploaded_images: |
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for image_path in uploaded_images: |
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_, encoded = process_image(image_path) |
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messages.append({"role": "user", "content": format_message("وصف الصورة:", encoded)}) |
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messages.append({"role": "user", "content": user_input}) |
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completion = client.chat.completions.create( |
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model="llama-3.3-70b-versatile", |
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messages=messages, |
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temperature=0.7, |
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max_tokens=1024, |
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top_p=1, |
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stream=True |
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) |
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response = "" |
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for chunk in completion: |
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if chunk.choices[0].delta.content: |
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response += chunk.choices[0].delta.content |
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time.sleep(0.01) |
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chat_history.append({"role": "user", "content": user_input}) |
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chat_history.append({"role": "assistant", "content": response}) |
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return chat_history, uploaded_images |
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with gr.Blocks(theme=gr.themes.Soft()) as demo: |
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gr.Markdown("# 🤖 ShfraAI - chatBot") |
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chatbot = gr.Chatbot(label="ShfraAI", avatar_images=(None, BOT_AVATAR), type="messages") |
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user_input = gr.Textbox(placeholder="اكتب رسالتك...", show_label=False) |
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upload_button = gr.UploadButton("رفع صور", file_count="multiple") |
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submit_button = gr.Button("إرسال") |
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uploaded_state = gr.State([]) |
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chat_history_state = gr.State([]) |
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upload_button.upload( |
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upload_images, |
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inputs=[upload_button, uploaded_state], |
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outputs=[chatbot, uploaded_state] |
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) |
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submit_button.click( |
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chat_with_gpt, |
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inputs=[user_input, uploaded_state, chat_history_state], |
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outputs=[chatbot, chat_history_state] |
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) |
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user_input.submit( |
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chat_with_gpt, |
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inputs=[user_input, uploaded_state, chat_history_state], |
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outputs=[chatbot, chat_history_state] |
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) |
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if __name__ == "__main__": |
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demo.queue().launch(share=True) |
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