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Update app.py
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app.py
CHANGED
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@@ -2,7 +2,7 @@ import gradio as gr
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import spaces
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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from googlesearch import search
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import requests
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from bs4 import BeautifulSoup
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@@ -20,7 +20,8 @@ def fetch_web_content(url):
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response = requests.get(url, timeout=10)
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soup = BeautifulSoup(response.text, 'html.parser')
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return ' '.join(p.get_text() for p in soup.find_all('p'))
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except:
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return "Could not fetch content from this URL"
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def web_search(query, num_results=3):
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@@ -34,7 +35,8 @@ def web_search(query, num_results=3):
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"content": content[:1000] # Limit content length
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})
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return results
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except:
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return []
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@spaces.GPU(duration=120)
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@@ -63,7 +65,7 @@ def generate_text(prompt, max_length, temperature, use_web):
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return tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True)
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#
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css = """
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:root {
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--primary: #e94560;
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@@ -96,25 +98,6 @@ body {
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border-radius: 15px 15px 0 0;
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text-align: center;
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margin-bottom: 2rem;
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position: relative;
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overflow: hidden;
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}
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.header::before {
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content: '';
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position: absolute;
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top: -50%;
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left: -50%;
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width: 200%;
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height: 200%;
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background: radial-gradient(circle, rgba(233,69,96,0.1) 0%, transparent 70%);
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animation: pulse 8s infinite;
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}
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@keyframes pulse {
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0% { transform: scale(0.8); opacity: 0.5; }
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50% { transform: scale(1.2); opacity: 0.2; }
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100% { transform: scale(0.8); opacity: 0.5; }
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}
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.header h1 {
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@@ -122,14 +105,6 @@ body {
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font-size: 2.8rem;
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margin-bottom: 1rem;
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font-weight: 700;
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position: relative;
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}
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.header p {
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color: #a0a0a0;
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font-size: 1.1rem;
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max-width: 800px;
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margin: 0 auto;
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}
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.input-group, .output-group {
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@@ -138,119 +113,33 @@ body {
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border-radius: 12px;
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margin-bottom: 2rem;
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border: 1px solid #2d2d4d;
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transition: transform 0.3s ease, box-shadow 0.3s ease;
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}
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.input-group:hover, .output-group:hover {
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transform: translateY(-2px);
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box-shadow: 0 6px 15px rgba(0, 0, 0, 0.3);
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}
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.generate-btn {
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background: linear-gradient(135deg, var(--primary) 0%, #c81e45 100%) !important;
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color: white !important;
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border: none !important;
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border-radius: 8px !important;
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padding: 12px 28px !important;
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font-size: 1.1rem !important;
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cursor: pointer !important;
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transition: transform 0.2s ease, box-shadow 0.2s ease !important;
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}
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.generate-btn:hover {
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transform: scale(1.05);
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box-shadow: 0 4px 15px rgba(233, 69, 96, 0.4) !important;
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}
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.example-prompts {
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background-color: #1f2b47;
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padding: 1.5rem;
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border-radius: 12px;
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margin-bottom: 2rem;
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border: 1px solid #3d3d6d;
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}
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.example-prompts h3 {
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color: var(--primary);
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margin-bottom: 1rem;
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font-size: 1.3rem;
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}
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.example-prompts ul {
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list-style: none;
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padding: 0;
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display: grid;
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grid-template-columns: repeat(auto-fit, minmax(250px, 1fr));
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gap: 1rem;
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}
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.example-prompts li {
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background-color: var(--secondary);
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padding: 1rem;
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border-radius: 8px;
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cursor: pointer;
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transition: all 0.2s ease;
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border: 1px solid #3d3d6d;
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}
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.example-prompts li:hover {
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background-color: #2d2d4d;
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transform: translateY(-2px);
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box-shadow: 0 4px 10px rgba(0, 0, 0, 0.2);
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}
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.param-group {
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display: grid;
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grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
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gap: 1.5rem;
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margin-bottom: 1.5rem;
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}
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.web-toggle {
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display: flex;
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align-items: center;
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gap: 1rem;
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padding: 1rem;
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background-color: var(--secondary);
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border-radius: 8px;
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margin-bottom: 1.5rem;
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}
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.output-controls {
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display: flex;
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gap: 1rem;
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margin-top: 1.5rem;
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}
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#copy-btn {
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background: #2d2d4d !important;
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border: 1px solid #3d3d6d !important;
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}
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#copy-btn:hover {
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background: #3d3d6d !important;
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}
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"""
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example_prompts = [
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"Explain quantum computing in simple terms",
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"Latest developments in AI research",
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"How does blockchain technology work?",
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"Compare React and Vue.js frameworks",
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"
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"Impact of climate change on marine life",
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"Recent advancements in cancer treatment",
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"Guide to starting a tech startup in 2024"
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]
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with gr.Blocks(css=css, theme=gr.themes.Default()) as iface:
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gr.HTML("""
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<div class="header">
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<h1>Llama-3.1-Storm-8B AI Assistant</h1>
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<p>Enhanced with real-time web search capabilities
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<img src="https://cdn-uploads.huggingface.co/production/uploads/64c75c1237333ccfef30a602/tmOlbERGKP7JSODa6T06J.jpeg"
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alt="Llama"
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style="border-radius: 12px; margin: 1.5rem 0;">
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</div>
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""")
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with gr.Column(scale=3):
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with gr.Group(elem_classes="example-prompts"):
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gr.Markdown("## Example Queries")
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example_btns = [gr.Button(prompt
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with gr.Group(elem_classes="input-group"):
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prompt = gr.Textbox(label="Your Query", placeholder="Enter your question
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lines=5, elem_id="main-input")
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with gr.
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web_search_toggle = gr.Checkbox(label="Enable Web Search", value=False)
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num_results = gr.Slider(1, 5, value=3, step=1, label="Search Results
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with gr.
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max_length = gr.Slider(32, 1024, value=256, step=32, label="Response Length")
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temperature = gr.Slider(0.1, 2.0, value=0.7, step=0.1, label="Creativity")
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with gr.Column(scale=2):
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with gr.Group(elem_classes="output-group"):
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output = gr.Textbox(label="Generated Response", lines=12
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with gr.
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copy_btn = gr.Button("Copy
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clear_btn = gr.Button("Clear
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with gr.TabItem("Web
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web_results = gr.JSON(label="Search Results Preview"
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# Event
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generate_btn.click(
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generate_text,
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inputs=[prompt, max_length, temperature, web_search_toggle],
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for btn in example_btns:
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btn.click(lambda x: x, inputs=[btn], outputs=[prompt])
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copy_btn.click(
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None,
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)
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clear_btn.click(lambda: "", outputs=[output])
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iface.launch()
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import spaces
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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from googlesearch import search
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import requests
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from bs4 import BeautifulSoup
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response = requests.get(url, timeout=10)
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soup = BeautifulSoup(response.text, 'html.parser')
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return ' '.join(p.get_text() for p in soup.find_all('p'))
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except Exception as e:
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print(f"Error fetching {url}: {str(e)}")
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return "Could not fetch content from this URL"
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def web_search(query, num_results=3):
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"content": content[:1000] # Limit content length
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})
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return results
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except Exception as e:
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print(f"Search error: {str(e)}")
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return []
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@spaces.GPU(duration=120)
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return tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True)
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# CSS and UI components
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css = """
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:root {
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--primary: #e94560;
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border-radius: 15px 15px 0 0;
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text-align: center;
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margin-bottom: 2rem;
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}
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.header h1 {
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font-size: 2.8rem;
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margin-bottom: 1rem;
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font-weight: 700;
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}
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.input-group, .output-group {
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border-radius: 12px;
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margin-bottom: 2rem;
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border: 1px solid #2d2d4d;
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}
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.generate-btn {
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background: linear-gradient(135deg, var(--primary) 0%, #c81e45 100%) !important;
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color: white !important;
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border-radius: 8px !important;
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padding: 12px 28px !important;
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}
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.example-prompts ul {
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grid-template-columns: repeat(auto-fit, minmax(250px, 1fr));
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gap: 1rem;
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}
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"""
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example_prompts = [
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"Explain quantum computing in simple terms",
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"Latest developments in AI research",
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"Compare React and Vue.js frameworks",
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"Recent advancements in cancer treatment"
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]
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with gr.Blocks(css=css, theme=gr.themes.Default()) as iface:
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gr.HTML("""
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<div class="header">
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<h1>Llama-3.1-Storm-8B AI Assistant</h1>
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<p>Enhanced with real-time web search capabilities</p>
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</div>
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""")
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with gr.Column(scale=3):
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with gr.Group(elem_classes="example-prompts"):
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gr.Markdown("## Example Queries")
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example_btns = [gr.Button(prompt) for prompt in example_prompts]
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with gr.Group(elem_classes="input-group"):
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prompt = gr.Textbox(label="Your Query", placeholder="Enter your question...", lines=5)
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with gr.Row():
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web_search_toggle = gr.Checkbox(label="Enable Web Search", value=False)
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num_results = gr.Slider(1, 5, value=3, step=1, label="Search Results")
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with gr.Row():
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max_length = gr.Slider(32, 1024, value=256, step=32, label="Response Length")
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temperature = gr.Slider(0.1, 2.0, value=0.7, step=0.1, label="Creativity")
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with gr.Column(scale=2):
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with gr.Group(elem_classes="output-group"):
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output = gr.Textbox(label="Generated Response", lines=12)
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with gr.Row():
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copy_btn = gr.Button("Copy")
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clear_btn = gr.Button("Clear")
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with gr.TabItem("Web Results"):
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web_results = gr.JSON(label="Search Results Preview")
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# Event handlers
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generate_btn.click(
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generate_text,
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inputs=[prompt, max_length, temperature, web_search_toggle],
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for btn in example_btns:
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btn.click(lambda x: x, inputs=[btn], outputs=[prompt])
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copy_btn.click(
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None,
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inputs=[output],
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_js="(text) => { navigator.clipboard.writeText(text); return [] }"
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)
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clear_btn.click(lambda: "", outputs=[output])
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iface.launch()
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