Update app.py

app.py

@@ -6,15 +6,18 @@ import asyncio
 import ipaddress
 from typing import Tuple
 
-
+# تعيين المتغيرات البيئية لتهيئة PyTorch لاستخدام الـ GPU إذا كان متاحًا
 os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
 
 # الحصول على التوكن من البيئة
 token = os.getenv("HF_TOKEN")
-Najeb_pipeline = pipeline("text-generation", model="sohiebwedyan/NAJEB_BOT", token=token, device=0 if torch.cuda.is_available() else -1)
-gpt2_pipeline = pipeline("text-generation", model="Qwen/Qwen-1_8B-Chat", device=0 if torch.cuda.is_available() else -1, trust_remote_code=True)
-llama2_pipeline = pipeline("text-generation", model="Harikrishnan46624/finetuned_llama2-1.1b-chat", device=0 if torch.cuda.is_available() else -1)
-summarization_pipeline = pipeline("summarization", model="Falconsai/text_summarization", device=0 if torch.cuda.is_available() else -1)
+
+# إعداد الأنابيب للموديلات المختلفة باستخدام PyTorch
+device = 0 if torch.cuda.is_available() else -1
+Najeb_pipeline = pipeline("text-generation", model="sohiebwedyan/NAJEB_BOT", token=token, device=device)
+gpt2_pipeline = pipeline("text-generation", model="Qwen/Qwen-1_8B-Chat", device=device, trust_remote_code=True)
+llama2_pipeline = pipeline("text-generation", model="Harikrishnan46624/finetuned_llama2-1.1b-chat", device=device)
+summarization_pipeline = pipeline("summarization", model="Falconsai/text_summarization", device=device)
 
 previous_questions = []
 
@@ -61,12 +64,15 @@ async def generate_llama2(question, max_length, num_beams, temperature):
 async def generate_responses_async(question, max_length=128, num_beams=2, temperature=0.5):
     previous_questions.append(question)
 
+    # إنشاء المهام بشكل غير متزامن لتوليد الردود من الموديلات المختلفة
     gpt2_task = asyncio.create_task(generate_gpt2(question, max_length, num_beams, temperature))
     Najeb_task = asyncio.create_task(generate_Najeb(question, max_length, num_beams, temperature))
     llama2_task = asyncio.create_task(generate_llama2(question, max_length, num_beams, temperature))
 
+    # تجميع الردود من جميع الموديلات
     gpt2_response, Najeb_response, llama2_response = await asyncio.gather(gpt2_task, Najeb_task, llama2_task)
 
+    # دمج الردود و تلخيصها
     combined_responses = f"GPT-2: {gpt2_response}\nNajeb: {Najeb_response}\nLLaMA 2: {llama2_response}"
     summarized_response = summarization_pipeline(combined_responses, max_length=150, min_length=50, do_sample=False)[0]['summary_text']
 
@@ -209,67 +215,40 @@ p {
 }
 
 #image-container img {
-    width: 1400px;
-    border-radius: 10px;
-    box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
+    width: 100%;
+    max-width: 500px;
+    margin-bottom: 10px;
 }
 
 #image-container button {
     position: absolute;
-    top: 50%;
-    left: 50%;
-    transform: translate(-50%, -50%);
-    background-color: rgba(0, 102, 204, 0.8);
+    top: 10px;
+    left: 10px;
+    background-color: rgba(0, 0, 0, 0.5);
     color: white;
     border: none;
-    padding: 10px 20px;
-    border-radius: 5px;
+    padding: 5px 10px;
     cursor: pointer;
-    font-size: 16px;
-    transition: background-color 0.3s ease;
-}
-
-#image-container button:hover {
-    background-color: rgba(0, 76, 153, 0.8);
-}
-
-.gr-box {
-    background-color: #ffffff;
-    border: 2px solid #0066cc;
-    border-radius: 10px;
-    padding: 10px;
-    margin-top: 10px;
-}
-#GPT2_output, #Najeb_output, #Llama2_output {
-    width: 400px;
-    height: 120px;
-}
-#summary_box {
-    background-color: rgba(255, 255, 255, 0.9);
 }
 """
 
-# إنشاء واجهة Gradio
-with gr.Blocks(css=custom_css) as iface:
-    with gr.Row():
-        with gr.Column():
-            question_input = gr.Textbox(label="Enter Your Question", placeholder="Ask a question here...", lines=3)
-            mode_selector = gr.Dropdown(
-                label="Select Mode", choices=["AI Question Answering", "IP Subnet Calculation"], value="AI Question Answering"
-            )
-            max_length_slider = gr.Slider(50, 500, step=10, label="Max Length", value=150)
-            num_beams_slider = gr.Slider(1, 5, step=1, label="Number of Beams", value=2)
-            temperature_slider = gr.Slider(0.0, 1.0, step=0.05, label="Temperature", value=0.5)
-
-        with gr.Column():
-            gpt2_output = gr.Textbox(label="GPT-2 Output", interactive=False)
-            Najeb_output = gr.Textbox(label="Najeb Output", interactive=False)
-            Llama2_output = gr.Textbox(label="Llama2 Output", interactive=False)
-            summarized_output = gr.Textbox(label="Summarized Answer", interactive=False)
-            previous_questions_output = gr.Textbox(label="Previous Questions", interactive=False)
-
-    mode_selector.change(handle_mode_selection, inputs=[mode_selector, question_input, max_length_slider, num_beams_slider, temperature_slider],
-                         outputs=[gpt2_output, Najeb_output, Llama2_output, summarized_output, previous_questions_output])
-
-# إطلاق الواجهة
-iface.launch(share=True)
+# إعداد واجهة Gradio
+gr.Interface(
+    fn=handle_mode_selection,
+    inputs=[
+        gr.Dropdown(choices=["AI Question Answering", "Subnet Calculation"], label="Select Mode"),
+        gr.Textbox(label="Input", placeholder="Ask your question or enter an IP address/subnet..."),
+        gr.Slider(minimum=50, maximum=1024, step=1, value=128, label="Max Length"),
+        gr.Slider(minimum=1, maximum=10, step=1, value=2, label="Num Beams"),
+        gr.Slider(minimum=0.0, maximum=1.0, step=0.1, value=0.5, label="Temperature")
+    ],
+    outputs=[
+        gr.Markdown(label="GPT-2 Answer"),
+        gr.Markdown(label="Najeb Answer"),
+        gr.Markdown(label="LLaMA 2 Answer"),
+        gr.Markdown(label="Summarized Answer"),
+        gr.Markdown(label="Previous Questions")
+    ],
+    css=custom_css,
+    live=True
+).launch(debug=True)