Update app.py

app.py

@@ -5,24 +5,32 @@ import gradio as gr
 import asyncio
 import ipaddress
 from typing import Tuple
+from accelerate import Accelerator
 
-# 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")
+accelerator = Accelerator()
+
+gpt2_pipeline = accelerator.prepare(
+    pipeline("text-generation", model="Qwen/Qwen-1_8B-Chat", device=accelerator.device)
+)
+Najeb_pipeline = accelerator.prepare(
+    pipeline("text-generation", model="najeebjust/Najeeb", device=accelerator.device)
+)
+llama2_pipeline = accelerator.prepare(
+    pipeline("text-generation", model="Harikrishnan46624/finetuned_llama2-1.1b-chat", device=accelerator.device)
+)
+'''
+gpt2_pipeline = pipeline("text-generation", model="Qwen/Qwen-1_8B-Chat", device=0 if torch.cuda.is_available() else -1, trust_remote_code=True)
+Najeb_pipeline = pipeline("text-generation", model="najeebjust/Najeeb", device=0 if torch.cuda.is_available() else -1)
+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)
-gpt2_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 = []
 
 
-# توليد الردود باستخدام GPT-2
 async def generate_gpt2(question, max_length, num_beams, temperature):
     return gpt2_pipeline(
         question,
@@ -30,12 +38,11 @@ async def generate_gpt2(question, max_length, num_beams, temperature):
         num_return_sequences=1,
         num_beams=num_beams,
         do_sample=True,
-        top_k=50,
-        top_p=0.95,
+        top_k=30,
+        top_p=0.9,
         temperature=temperature
     )[0]['generated_text']
 
-# توليد الردود باستخدام Najeb
 async def generate_Najeb(question, max_length, num_beams, temperature):
     return Najeb_pipeline(
         question,
@@ -47,9 +54,7 @@ async def generate_Najeb(question, max_length, num_beams, temperature):
         top_p=0.85,
         temperature=temperature
     )[0]['generated_text']
-    
-'''
-# توليد الردود باستخدام LLaMA 2
+
 async def generate_llama2(question, max_length, num_beams, temperature):
     return llama2_pipeline(
         question,
@@ -57,51 +62,44 @@ async def generate_llama2(question, max_length, num_beams, temperature):
         num_return_sequences=1,
         num_beams=num_beams,
         do_sample=True,
-        top_k=50,
-        top_p=0.95,
+        top_k=30,
+        top_p=0.9,
         temperature=temperature
-    )[0]['generated_text']'''
+    )[0]['generated_text']
+
 
-# التعامل مع الردود بشكل غير متزامن
 async def generate_responses_async(question, max_length=128, num_beams=2, temperature=0.5):
+    responses = {}
+
+
     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))
+    llama2_task = asyncio.create_task(generate_llama2(question, max_length, num_beams, temperature))
+
+    gpt2_response, Najeb_response, llama2_response = await asyncio.gather(gpt2_task, Najeb_task, llama2_task)
+
+    responses['GPT-2'] = gpt2_response
+    responses['Najeb '] = Najeb_response
+    responses['LLaMA 2'] = llama2_response
+
+
+    combined_responses = f"GPT-2: {gpt2_response}\nNajeb: {Najeb_response}\nLLaMA 2: {llama2_response}"
 
-    # تجميع الردود من جميع الموديلات
-    gpt2_response, Najeb_response = await asyncio.gather(gpt2_task, Najeb_task, llama2_task)
 
-    # دمج الردود و تلخيصها
-    combined_responses = f"GPT-2: {gpt2_response}\nNajeb: {Najeb_response}"
     summarized_response = summarization_pipeline(combined_responses, max_length=150, min_length=50, do_sample=False)[0]['summary_text']
 
     return {
-        "GPT-2 Answer": gpt2_response,
-        "Najeb Answer": Najeb_response,
-        #"LLaMA 2 Answer": llama2_response,
-        "Summarized Answer": summarized_response,
+
+        "Najeb Answering Response": Najeb_response,
+        "GPT-2 Answering Response": gpt2_response,
+        "LLaMA 2 Answering Response": llama2_response,
+        "Summarized Answering Response": summarized_response,
         "Previous Questions": "\n".join(previous_questions[-5:])
     }
 
-# تحديد طريقة الحساب بناءً على المدخل
-def handle_mode_selection(mode, input_text, max_length, num_beams, temperature):
-    if mode == "AI Question Answering":
-        result = asyncio.run(generate_responses_async(input_text, max_length, num_beams, temperature))
-        return (
-            f"**GPT-2 Model Response:**\n{result['GPT-2 Answer']}",
-            f"**Najeb Model Response:**\n{result['Najeb Answer']}",
-            #f"**LLaMA 2 Model Response:**\n{result['LLaMA 2 Answer']}",
-            f"**Summarized Response:**\n{result['Summarized Answer']}",
-            f"**Previous Questions:**\n{result['Previous Questions']}"
-        )
-    else:
-        subnet_result = calculate_subnet(input_text)
-        return subnet_result, "", "", "", ""
 
-# الحصول على الشبكة وعنوان الـ IP
 def get_network(ip_input: str) -> Tuple[ipaddress.IPv4Network, str]:
     try:
         if ip_input.count("/") == 0:
@@ -112,18 +110,19 @@ def get_network(ip_input: str) -> Tuple[ipaddress.IPv4Network, str]:
     except ValueError:
         return None, None
 
-# حساب الشبكة الفرعية
 def calculate_subnet(ip_input: str) -> str:
     network, ip = get_network(ip_input)
     if network is None or ip is None:
         return "Invalid IP Address or Subnet!"
 
+
     network_address = network.network_address
     broadcast_address = network.broadcast_address
     usable_hosts = list(network.hosts())
     num_usable_hosts = len(usable_hosts)
     usable_hosts_range = f"{usable_hosts[0]} - {usable_hosts[-1]}" if usable_hosts else "NA"
 
+
     octets = str(ip).split('.')
     binary_octets = [bin(int(octet))[2:].zfill(8) for octet in octets]
     bin_ip = '.'.join(binary_octets)
@@ -134,6 +133,7 @@ def calculate_subnet(ip_input: str) -> str:
     bin_mask = str(bin(int(network.netmask))[2:].zfill(32))
     bin_mask = '.'.join([bin_mask[i:i+8] for i in range(0, len(bin_mask), 8)])
 
+
     result = f"""
 IP Address:             {ip}
 Address (bin):          {bin_ip}
@@ -151,7 +151,17 @@ Private IP:             {network.is_private}
 """
     return result.strip()
 
-# تحديد التصميم المخصص
+
+
+def handle_mode_selection(mode, input_text, max_length, num_beams, temperature):
+    if mode == "AI Question Answering":
+        result = asyncio.run(generate_responses_async(input_text, max_length, num_beams, temperature))
+        return result, ""
+    else:
+        subnet_result = calculate_subnet(input_text)
+        return {"Subnet Calculation Result": subnet_result}, ""
+
+
 custom_css = """
 body {
     background-color: #f0f8ff;
@@ -211,46 +221,85 @@ p {
     box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
 }
 
+
 #image-container {
     text-align: center;
     position: relative;
 }
 
 #image-container img {
-    width: 100%;
-    max-width: 500px;
-    margin-bottom: 10px;
+    width: 1400px;
+    border-radius: 10px;
+    box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
 }
 
 #image-container button {
     position: absolute;
-    top: 10px;
-    left: 10px;
-    background-color: rgba(0, 0, 0, 0.5);
+    top: 50%;
+    left: 50%;
+    transform: translate(-50%, -50%);
+    background-color: rgba(0, 102, 204, 0.8);
     color: white;
     border: none;
-    padding: 5px 10px;
+    padding: 10px 20px;
+    border-radius: 5px;
     cursor: pointer;
+    font-size: 16px;
+    transition: background-color 0.3s ease;
+}
+
+#image-container button:hover {
+    background-color: rgba(0, 76, 153, 0.8);
 }
 """
 
-# 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)
+
+scroll_js = """
+<script>
+    function scrollToTop() {
+        document.getElementById('target-section').scrollIntoView({behavior: 'smooth'});
+    }
+</script>
+"""
+
+
+iface = gr.Blocks(css=custom_css)
+
+with iface:
+    gr.Markdown(f"<h1>Welcome to Najeb</h1><p>AI Question & Subnet Calculator, Enter your question or IP address to generate answers or calculate subnets.</p>")
+
+
+    gr.HTML(f"""
+    <div id="image-container">
+        <img src="https://news.cornell.edu/sites/default/files/styles/story_thumbnail_xlarge/public/2024-07/robot-1280x720_0.jpg?itok=AF6MakCq" alt="AI Image">
+        <button onclick="scrollToTop()">Go to Top</button>
+    </div>
+    {scroll_js}  <!-- Adding the JS to handle scrolling -->
+    """)
+
+
+    gr.Markdown("<div id='target-section'></div>")
+
+    with gr.Row():
+        mode_selector = gr.Radio(["AI Question Answering", "Subnet Calculation"], label="Select Mode", value="AI Question Answering")
+
+    with gr.Row():
+        with gr.Column():
+            input_text = gr.Textbox(label="Enter your question or IP", placeholder="Type here...", lines=2)
+            max_length_slider = gr.Slider(minimum=50, maximum=1024, value=128, label="Max Length")
+            num_beams_slider = gr.Slider(minimum=1, maximum=10, value=2, label="Number of Beams", step=1)
+            temperature_slider = gr.Slider(minimum=0.1, maximum=1.0, value=0.5, label="Temperature", step=0.1)
+            submit_button = gr.Button("Submit")
+
+        with gr.Column():
+            output_box = gr.JSON(label="Response Output")
+            previous_questions_box = gr.Markdown("### Previous Questions\n")
+
+    submit_button.click(
+        handle_mode_selection,
+        inputs=[mode_selector, input_text, max_length_slider, num_beams_slider, temperature_slider],
+        outputs=[output_box, previous_questions_box]
+    )
+
+
+iface.launch(share=True)