Update app.py

app.py

@@ -1,64 +1,295 @@
+import os
+import torch
+from transformers import pipeline
 import gradio as gr
-from huggingface_hub import InferenceClient
+import asyncio
+import ipaddress
+from typing import Tuple
 
+
+os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
+
+
+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="sohiebwedyan/NAJEB_BOT", 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)
+
+previous_questions = []
+
+async def generate_gpt2(question, max_length, num_beams, temperature):
+    return gpt2_pipeline(
+        question,
+        max_length=max_length,
+        num_return_sequences=1,
+        num_beams=num_beams,
+        do_sample=True,
+        top_k=50,
+        top_p=0.95,
+        temperature=temperature
+    )[0]['generated_text']
+
+async def generate_Najeb(question, max_length, num_beams, temperature):
+    return Najeb_pipeline(
+        question,
+        max_length=max_length,
+        num_return_sequences=1,
+        num_beams=num_beams,
+        do_sample=True,
+        top_k=30,
+        top_p=0.85,
+        temperature=temperature
+    )[0]['generated_text']
+
+async def generate_llama2(question, max_length, num_beams, temperature):
+    return llama2_pipeline(
+        question,
+        max_length=max_length,
+        num_return_sequences=1,
+        num_beams=num_beams,
+        do_sample=True,
+        top_k=30,
+        top_p=0.9,
+        temperature=temperature
+    )[0]['generated_text']
+
+async def generate_responses_async(question, max_length=128, num_beams=2, temperature=0.5):
+    previous_questions.append(question)
+
+
+    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))
+
+    gpt2_response, Najeb_response, llama2_response = await asyncio.gather(gpt2_task, Najeb_task, llama2_task)
+
+
+    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']
+
+    return {
+        "GPT-2 Answer": gpt2_response,
+        "Najeb Answer": Najeb_response,
+        "LLaMA 2 Answer": llama2_response,
+        "Summarized Answer": summarized_response,
+        "Previous Questions": "\n".join(previous_questions[-5:])
+    }
+
+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, "", "", "", ""
+def get_network(ip_input: str) -> Tuple[ipaddress.IPv4Network, str]:
+    try:
+        if ip_input.count("/") == 0:
+            ip_input += "/24"
+        net = ipaddress.IPv4Network(ip_input, strict=False)
+        ip = ip_input.split("/")[0]
+        return (net, ip)
+    except ValueError:
+        return None, None
+
+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)
+
+    bin_addr = str(bin(int(network_address))[2:].zfill(32))
+    bin_addr = '.'.join([bin_addr[i:i+8] for i in range(0, len(bin_addr), 8)])
+
+    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}
+Network Address:        {network_address}
+Network Address (bin):  {bin_addr}
+Netmask:                {network.netmask}
+Netmask (bin):          {bin_mask}
+CIDR Notation:          {network.prefixlen}
+Broadcast Address:      {broadcast_address}
+Usable IP Range:        {usable_hosts_range}
+Number of Hosts:        {network.num_addresses:,d}
+Number of Usable Hosts: {num_usable_hosts:,d}
+Wildcard Mask:          {network.hostmask}
+Private IP:             {network.is_private}
 """
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+    return result.strip()
+
+
+
+custom_css = """
+body {
+    background-color: #f0f8ff;
+    font-family: 'Arial', sans-serif;
+    color: #333;
+}
+
+h1 {
+    text-align: center;
+    color: #0066cc;
+}
 
+p {
+    text-align: center;
+    color: #333;
+}
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+.gradio-container {
+    width: 80%;
+    margin: auto;
+    background-color: rgba(255, 255, 255, 0.8);
+    box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
+    padding: 20px;
+    border-radius: 10px;
+}
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+.gr-button {
+    background-color: #0066cc;
+    color: white;
+    border: none;
+    border-radius: 5px;
+    padding: 10px;
+    cursor: pointer;
+    transition: background-color 0.3s ease;
+}
 
-    messages.append({"role": "user", "content": message})
+.gr-button:hover {
+    background-color: #004c99;
+}
 
-    response = ""
+.gr-textbox {
+    border: 2px solid #0066cc;
+    border-radius: 5px;
+    padding: 10px;
+    background-color: #fff;
+    color: #333;
+}
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
+.gr-slider {
+    color: #0066cc;
+}
 
-        response += token
-        yield response
+.gr-json {
+    background-color: rgba(240, 248, 255, 0.8);
+    border-radius: 10px;
+    padding: 10px;
+    box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
+}
 
 
+#image-container {
+    text-align: center;
+    position: relative;
+}
+
+#image-container img {
+    width: 1400px;
+    border-radius: 10px;
+    box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
+}
+
+#image-container button {
+    position: absolute;
+    top: 50%;
+    left: 50%;
+    transform: translate(-50%, -50%);
+    background-color: rgba(0, 102, 204, 0.8);
+    color: white;
+    border: none;
+    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);
+}
+
+.gr-box {
+    background-color: #ffffff;
+    border: 2px solid #0066cc;
+    border-radius: 10px;
+    padding: 10px;
+    margin-top: 10px;
+}
+#GPT2_title, #Najeb_title, #LLaMA_title, #summary_title, #previous_title {
+    color: black;
+    font-weight: bold;
+}
 """
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
+
+scroll_js = """
+<script>
+    function scrollToTop() {
+        document.getElementById('target-section').scrollIntoView({behavior: 'smooth'});
+    }
+</script>
 """
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-
-
-if __name__ == "__main__":
-    demo.launch()
+
+
+
+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 Najeb</button>
+    </div>
+    {scroll_js}  <!-- Adding the JS to handle scrolling -->
+    """)
+    
+
+    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(50, 1024, 128, label="Max Length")
+            num_beams_slider = gr.Slider(1, 10, 2, label="Number of Beams", step=1)
+            temperature_slider = gr.Slider(0.1, 1.0, 0.5, label="Temperature", step=0.1)
+            submit_button = gr.Button("Submit")
+
+        with gr.Column():
+            gpt2_output_box = gr.Markdown(label="GPT-2 Model Response")
+            najeb_output_box = gr.Markdown(label="Najeb Model Response")
+            llama2_output_box = gr.Markdown(label="LLaMA 2 Model Response")
+            summary_output_box = gr.Markdown(label="Summarized Response")
+            previous_questions_box = gr.Markdown(label="Previous Questions")
+
+    submit_button.click(
+        handle_mode_selection,
+        inputs=[mode_selector, input_text, max_length_slider, num_beams_slider, temperature_slider],
+        outputs=[gpt2_output_box, najeb_output_box, llama2_output_box, summary_output_box, previous_questions_box]
+    )
+
+iface.launch(share=True)