Create app.py

app.py

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+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/9121959e-df49-4796-a8b2-eb85f6368976.png"
+os.makedirs(UPLOAD_FOLDER, exist_ok=True)
+
+# إعدادات الصور
+IMAGE_WIDTH = 511
+
+def preprocess_image(image: Image.Image) -> Image.Image:
+    # ضبط مقاس الصورة
+    image_height = int(image.height * IMAGE_WIDTH / image.width)
+    return image.resize((IMAGE_WIDTH, image_height))
+
+def save_image(image: Image.Image) -> str:
+    # حفظ الصورة
+    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:
+    # تحويل وحفظ الصورة
+    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, history):
+    for f in files:
+        saved, _ = process_image(f)
+        history.append(("تم رفع صورة:", f"<img src='{saved}' style='max-width:200px;'>"))
+    return history
+
+def chat_with_gpt(user_input, history, files):
+    history = history or []
+    messages = [{"role": "system", "content": "...relevant prompt..."}]
+    if files:
+        for f in files:
+            saved, encoded = process_image(f)
+            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)
+    history.append((f"أنت: {user_input}", None))
+    history.append((None, f"<img src='{BOT_AVATAR}' style='width:50px;border-radius:50%;'> {response}"))
+    return history, history
+
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# 🤖 ShfraAI - مساعد ذكي لتحليل الصور")
+    chatbot = gr.Chatbot(label="ShfraAI").style(height=600)
+    user_input = gr.Textbox(placeholder="اكتب رسالتك...", show_label=False)
+    upload_button = gr.UploadButton("رفع صور", file_count="multiple")
+    submit_button = gr.Button("إرسال")
+    state = gr.State()
+
+    submit_button.click(
+        chat_with_gpt,
+        inputs=[user_input, state, upload_button],
+        outputs=[chatbot, state]
+    )
+    user_input.submit(
+        chat_with_gpt,
+        inputs=[user_input, state, upload_button],
+        outputs=[chatbot, state]
+    )
+    upload_button.upload(
+        upload_images,
+        inputs=[upload_button, state],
+        outputs=[chatbot]
+    )
+
+if __name__ == "__main__":
+    demo.queue().launch(share=True)