Update app.py

app.py

@@ -2,9 +2,7 @@ import gradio as gr
 import spaces
 import torch
 from transformers import AutoTokenizer, AutoModelForCausalLM
-from googlesearch import search
-import requests
-from bs4 import BeautifulSoup
+
 
 # Load the model and tokenizer
 model_name = "akjindal53244/Llama-3.1-Storm-8B"
@@ -15,43 +13,14 @@ model = AutoModelForCausalLM.from_pretrained(
     device_map="auto"
 )
 
-def fetch_web_content(url):
-    try:
-        response = requests.get(url, timeout=10)
-        soup = BeautifulSoup(response.text, 'html.parser')
-        return ' '.join(p.get_text() for p in soup.find_all('p'))
-    except Exception as e:
-        print(f"Error fetching {url}: {str(e)}")
-        return "Could not fetch content from this URL"
-
-def web_search(query, num_results=3):
-    try:
-        results = []
-        for j in search(query, num_results=num_results, advanced=True):
-            content = fetch_web_content(j.url)
-            results.append({
-                "title": j.title,
-                "url": j.url,
-                "content": content[:1000]  # Limit content length
-            })
-        return results
-    except Exception as e:
-        print(f"Search error: {str(e)}")
-        return []
-
 @spaces.GPU(duration=120)
-def generate_text(prompt, max_length, temperature, use_web):
-    if use_web:
-        search_results = web_search(prompt)
-        context = "\n".join([f"Source: {res['url']}\nContent: {res['content']}" for res in search_results])
-        prompt = f"Web Context:\n{context}\n\nUser Query: {prompt}"
-
+def generate_text(prompt, max_length, temperature):
     messages = [
-        {"role": "system", "content": "You are a helpful assistant with web search capabilities."},
+        {"role": "system", "content": "You are a helpful assistant."},
         {"role": "user", "content": prompt}
     ]
-    
     formatted_prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
+
     inputs = tokenizer(formatted_prompt, return_tensors="pt").to(model.device)
 
     outputs = model.generate(
@@ -65,135 +34,133 @@ def generate_text(prompt, max_length, temperature, use_web):
 
     return tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True)
 
-# CSS and UI components
-css = """
-:root {
-    --primary: #e94560;
-    --secondary: #1a1a2e;
-    --background: #16213e;
-    --text: #e0e0e0;
-}
 
+# Custom CSS
+css = """
 body {
-    background-color: var(--background);
-    color: var(--text);
-    font-family: 'Inter', sans-serif;
+    background-color: #1a1a2e;
+    color: #e0e0e0;
+    font-family: 'Arial', sans-serif;
 }
-
 .container {
-    max-width: 1200px;
+    max-width: 900px;
     margin: auto;
     padding: 20px;
 }
-
 .gradio-container {
-    background-color: var(--background);
+    background-color: #16213e;
     border-radius: 15px;
-    box-shadow: 0 4px 20px rgba(0, 0, 0, 0.3);
+    box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
 }
-
 .header {
-    background: linear-gradient(135deg, #0f3460 0%, #1a1a2e 100%);
-    padding: 2rem;
+    background-color: #0f3460;
+    padding: 20px;
     border-radius: 15px 15px 0 0;
     text-align: center;
-    margin-bottom: 2rem;
+    margin-bottom: 20px;
 }
-
 .header h1 {
-    color: var(--primary);
-    font-size: 2.8rem;
-    margin-bottom: 1rem;
-    font-weight: 700;
+    color: #e94560;
+    font-size: 2.5em;
+    margin-bottom: 10px;
+}
+.header p {
+    color: #a0a0a0;
+}
+.header img {
+    max-width: 300px;
+    border-radius: 10px;
+    margin: 15px auto;
+    display: block;
 }
-
 .input-group, .output-group {
-    background-color: var(--secondary);
-    padding: 2rem;
-    border-radius: 12px;
-    margin-bottom: 2rem;
-    border: 1px solid #2d2d4d;
+    background-color: #1a1a2e;
+    padding: 20px;
+    border-radius: 10px;
+    margin-bottom: 20px;
+}
+.input-group label, .output-group label {
+    color: #e94560;
+    font-weight: bold;
 }
-
 .generate-btn {
-    background: linear-gradient(135deg, var(--primary) 0%, #c81e45 100%) !important;
+    background-color: #e94560 !important;
     color: white !important;
-    border-radius: 8px !important;
-    padding: 12px 28px !important;
+    border: none !important;
+    border-radius: 5px !important;
+    padding: 10px 20px !important;
+    font-size: 16px !important;
+    cursor: pointer !important;
+    transition: background-color 0.3s ease !important;
+}
+.generate-btn:hover {
+    background-color: #c81e45 !important;
+}
+.example-prompts {
+    background-color: #1f2b47;
+    padding: 15px;
+    border-radius: 10px;
+    margin-bottom: 20px;
+}
+.example-prompts h3 {
+    color: #e94560;
+    margin-bottom: 10px;
 }
-
 .example-prompts ul {
-    grid-template-columns: repeat(auto-fit, minmax(250px, 1fr));
-    gap: 1rem;
+    list-style-type: none;
+    padding-left: 0;
+}
+.example-prompts li {
+    margin-bottom: 5px;
+    cursor: pointer;
+    transition: color 0.3s ease;
+}
+.example-prompts li:hover {
+    color: #e94560;
 }
 """
 
+# Example prompts
 example_prompts = [
-    "Explain quantum computing in simple terms",
-    "Latest developments in AI research",
-    "Compare React and Vue.js frameworks",
-    "Recent advancements in cancer treatment"
+    "Write a Python function to find the n-th Fibonacci number.",
+    "Explain the concept of recursion in programming.",
+    "What are the key differences between Python and JavaScript?",
+    "Tell me a short story about a time-traveling robot.",
+    "Describe the process of photosynthesis in simple terms."
 ]
 
-with gr.Blocks(css=css, theme=gr.themes.Default()) as iface:
-    gr.HTML("""
+# Gradio interface
+# Gradio interface
+with gr.Blocks(css=css) as iface:
+    gr.HTML(
+        """
         <div class="header">
-            <h1>Llama-3.1-Storm-8B AI Assistant</h1>
-            <p>Enhanced with real-time web search capabilities</p>
+            <h1>Llama-3.1-Storm-8B Text Generation</h1>
+            <p>Generate text using the powerful Llama-3.1-Storm-8B model. Enter a prompt and let the AI create!</p>
+            <img src="https://cdn-uploads.huggingface.co/production/uploads/64c75c1237333ccfef30a602/tmOlbERGKP7JSODa6T06J.jpeg" alt="Llama">
         </div>
-    """)
-
-    with gr.Tabs():
-        with gr.TabItem("Chat Assistant"):
-            with gr.Row():
-                with gr.Column(scale=3):
-                    with gr.Group(elem_classes="example-prompts"):
-                        gr.Markdown("## Example Queries")
-                        example_btns = [gr.Button(prompt) for prompt in example_prompts]
-
-                    with gr.Group(elem_classes="input-group"):
-                        prompt = gr.Textbox(label="Your Query", placeholder="Enter your question...", lines=5)
-                        
-                        with gr.Row():
-                            web_search_toggle = gr.Checkbox(label="Enable Web Search", value=False)
-                            num_results = gr.Slider(1, 5, value=3, step=1, label="Search Results")
-                        
-                        with gr.Row():
-                            max_length = gr.Slider(32, 1024, value=256, step=32, label="Response Length")
-                            temperature = gr.Slider(0.1, 2.0, value=0.7, step=0.1, label="Creativity")
-                        
-                        generate_btn = gr.Button("Generate Response", elem_classes="generate-btn")
-
-                with gr.Column(scale=2):
-                    with gr.Group(elem_classes="output-group"):
-                        output = gr.Textbox(label="Generated Response", lines=12)
-                        with gr.Row():
-                            copy_btn = gr.Button("Copy")
-                            clear_btn = gr.Button("Clear")
-
-        with gr.TabItem("Web Results"):
-            web_results = gr.JSON(label="Search Results Preview")
-
-    # Event handlers
-    generate_btn.click(
-        generate_text,
-        inputs=[prompt, max_length, temperature, web_search_toggle],
-        outputs=output
-    ).then(
-        lambda q: web_search(q) if q else [],
-        inputs=[prompt],
-        outputs=web_results
+        """
     )
 
-    for btn in example_btns:
-        btn.click(lambda x: x, inputs=[btn], outputs=[prompt])
+    with gr.Group():
+        with gr.Group(elem_classes="example-prompts"):
+            gr.HTML("<h3>Example Prompts:</h3>")
+            example_buttons = [gr.Button(prompt) for prompt in example_prompts]
 
-    copy_btn.click(
-        None,
-        inputs=[output],
-        _js="(text) => { navigator.clipboard.writeText(text); return [] }"
-    )
-    
-    clear_btn.click(lambda: "", outputs=[output])
+        with gr.Group(elem_classes="input-group"):
+            prompt = gr.Textbox(label="Prompt", placeholder="Enter your prompt here...", lines=5)
+            max_length = gr.Slider(minimum=1, maximum=500, value=128, step=1, label="Max Length")
+            temperature = gr.Slider(minimum=0.1, maximum=2.0, value=0.7, step=0.1, label="Temperature")
+            generate_btn = gr.Button("Generate", elem_classes="generate-btn")
+
+        with gr.Group(elem_classes="output-group"):
+            output = gr.Textbox(label="Generated Text", lines=10)
+
+    generate_btn.click(generate_text, inputs=[prompt, max_length, temperature], outputs=output)
+
+    # Set up example prompt buttons
+    for button in example_buttons:
+        button.click(lambda x: x, inputs=[button], outputs=[prompt])
 
+# Launch the app
 iface.launch()