app.py

import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig
import spaces
from duckduckgo_search import DDGS
import time
import torch
from datetime import datetime
import gc  # For manual garbage collection

# Initialize model and tokenizer with optimizations
model_name = "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B"

# Load config first to set optimal parameters
config = AutoConfig.from_pretrained(model_name)
config.use_cache = True  # Enable KV-caching for faster inference

# Initialize tokenizer with optimizations
tokenizer = AutoTokenizer.from_pretrained(
    model_name,
    model_max_length=256,  # Reduced for faster processing
    padding_side="left",
    truncation_side="left",
)
tokenizer.pad_token = tokenizer.eos_token

# Load model with optimizations
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    config=config,
    device_map="cpu",
    low_cpu_mem_usage=True,
    torch_dtype=torch.float32,
)

# Enable model optimizations
model.eval()  # Set to evaluation mode
torch.set_num_threads(4)  # Limit CPU threads for better performance

def get_web_results(query, max_results=3):  # Reduced max results
    """Get web search results using DuckDuckGo"""
    try:
        with DDGS() as ddgs:
            results = list(ddgs.text(query, max_results=max_results))
            return [{
                "title": result.get("title", ""),
                "snippet": result["body"][:200],  # Limit snippet length
                "url": result["href"],
                "date": result.get("published", "")
            } for result in results]
    except Exception as e:
        return []

def format_prompt(query, context):
    """Format the prompt with web context - optimized version"""
    context_lines = '\n'.join([f'[{i+1}] {res["snippet"]}' 
                              for i, res in enumerate(context)])
    return f"""Answer this query using the context: {query}\n\nContext:\n{context_lines}\n\nAnswer:"""

def format_sources(web_results):
    """Format sources with more details"""
    if not web_results:
        return "<div class='no-sources'>No sources available</div>"
    
    sources_html = "<div class='sources-container'>"
    for i, res in enumerate(web_results, 1):
        title = res["title"] or "Source"
        date = f"<span class='source-date'>{res['date']}</span>" if res['date'] else ""
        sources_html += f"""
        <div class='source-item'>
            <div class='source-number'>[{i}]</div>
            <div class='source-content'>
                <a href="{res['url']}" target="_blank" class='source-title'>{title}</a>
                {date}
                <div class='source-snippet'>{res['snippet'][:150]}...</div>
            </div>
        </div>
        """
    sources_html += "</div>"
    return sources_html

def generate_answer(prompt):
    """Generate answer using the DeepSeek model - optimized version"""
    try:
        # Clear CUDA cache and garbage collect
        if torch.cuda.is_available():
            torch.cuda.empty_cache()
        gc.collect()
        
        inputs = tokenizer(
            prompt, 
            return_tensors="pt",
            padding=True,
            truncation=True,
            max_length=256,
            return_attention_mask=True
        )
        
        with torch.no_grad():  # Disable gradient calculation
            outputs = model.generate(
                inputs.input_ids,
                attention_mask=inputs.attention_mask,
                max_new_tokens=100,  # Further reduced for speed
                temperature=0.7,
                top_p=0.95,
                pad_token_id=tokenizer.eos_token_id,
                do_sample=True,
                num_beams=1,
                early_stopping=True,
                no_repeat_ngram_size=3,
                length_penalty=1.0
            )
        
        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
        return response.split('Answer:')[-1].strip()
    
    except Exception as e:
        return f"Error generating response: {str(e)}"

def process_query(query, history):
    """Process user query with optimized streaming effect"""
    try:
        if history is None:
            history = []
        
        # Get web results first
        web_results = get_web_results(query)
        sources_html = format_sources(web_results)
        
        # Show searching status
        yield {
            answer_output: gr.Markdown("*Searching and generating response...*"),
            sources_output: gr.HTML(sources_html),
            search_btn: gr.Button("Please wait...", interactive=False),
            chat_history_display: history + [[query, "*Processing...*"]]
        }
        
        # Generate answer with timeout protection
        prompt = format_prompt(query, web_results)
        answer = generate_answer(prompt)
        
        # Update with final answer
        final_history = history + [[query, answer]]
        yield {
            answer_output: gr.Markdown(answer),
            sources_output: gr.HTML(sources_html),
            search_btn: gr.Button("Search", interactive=True),
            chat_history_display: final_history
        }
        
    except Exception as e:
        error_msg = f"Error: {str(e)}"
        yield {
            answer_output: gr.Markdown(error_msg),
            sources_output: gr.HTML("<div>Error fetching sources</div>"),
            search_btn: gr.Button("Search", interactive=True),
            chat_history_display: history + [[query, error_msg]]
        }

# Update the CSS for better contrast and readability
css = """
.gradio-container {
    max-width: 1200px !important;
    background-color: #f7f7f8 !important;
}

#header {
    text-align: center;
    margin-bottom: 2rem;
    padding: 2rem 0;
    background: #1a1b1e;
    border-radius: 12px;
    color: white;
}

#header h1 {
    color: white;
    font-size: 2.5rem;
    margin-bottom: 0.5rem;
}

#header h3 {
    color: #a8a9ab;
}

.search-container {
    background: #1a1b1e;
    border-radius: 12px;
    box-shadow: 0 4px 12px rgba(0,0,0,0.1);
    padding: 1rem;
    margin-bottom: 1rem;
}

.search-box {
    padding: 1rem;
    background: #2c2d30;
    border-radius: 8px;
    margin-bottom: 1rem;
}

/* Style the input textbox */
.search-box input[type="text"] {
    background: #3a3b3e !important;
    border: 1px solid #4a4b4e !important;
    color: white !important;
    border-radius: 8px !important;
}

.search-box input[type="text"]::placeholder {
    color: #a8a9ab !important;
}

/* Style the search button */
.search-box button {
    background: #2563eb !important;
    border: none !important;
}

/* Results area styling */
.results-container {
    background: #2c2d30;
    border-radius: 8px;
    padding: 1rem;
    margin-top: 1rem;
}

.answer-box {
    background: #3a3b3e;
    border-radius: 8px;
    padding: 1.5rem;
    color: white;
    margin-bottom: 1rem;
}

.answer-box p {
    color: #e5e7eb;
    line-height: 1.6;
}

.sources-container {
    margin-top: 1rem;
    background: #2c2d30;
    border-radius: 8px;
    padding: 1rem;
}

.source-item {
    display: flex;
    padding: 12px;
    margin: 8px 0;
    background: #3a3b3e;
    border-radius: 8px;
    transition: all 0.2s;
}

.source-item:hover {
    background: #4a4b4e;
}

.source-number {
    font-weight: bold;
    margin-right: 12px;
    color: #60a5fa;
}

.source-content {
    flex: 1;
}

.source-title {
    color: #60a5fa;
    font-weight: 500;
    text-decoration: none;
    display: block;
    margin-bottom: 4px;
}

.source-date {
    color: #a8a9ab;
    font-size: 0.9em;
    margin-left: 8px;
}

.source-snippet {
    color: #e5e7eb;
    font-size: 0.9em;
    line-height: 1.4;
}

.chat-history {
    max-height: 400px;
    overflow-y: auto;
    padding: 1rem;
    background: #2c2d30;
    border-radius: 8px;
    margin-top: 1rem;
}

.examples-container {
    background: #2c2d30;
    border-radius: 8px;
    padding: 1rem;
    margin-top: 1rem;
}

.examples-container button {
    background: #3a3b3e !important;
    border: 1px solid #4a4b4e !important;
    color: #e5e7eb !important;
}

/* Markdown content styling */
.markdown-content {
    color: #e5e7eb !important;
}

.markdown-content h1, .markdown-content h2, .markdown-content h3 {
    color: white !important;
}

.markdown-content a {
    color: #60a5fa !important;
}

/* Accordion styling */
.accordion {
    background: #2c2d30 !important;
    border-radius: 8px !important;
    margin-top: 1rem !important;
}
"""

# Update the Gradio interface layout
with gr.Blocks(title="AI Search Assistant", css=css, theme="dark") as demo:
    chat_history = gr.State([])
    
    with gr.Column(elem_id="header"):
        gr.Markdown("# 🔍 AI Search Assistant")
        gr.Markdown("### Powered by DeepSeek & Real-time Web Results")
    
    with gr.Column(elem_classes="search-container"):
        with gr.Row(elem_classes="search-box"):
            search_input = gr.Textbox(
                label="", 
                placeholder="Ask anything...", 
                scale=5,
                container=False
            )
            search_btn = gr.Button("Search", variant="primary", scale=1)
        
        with gr.Row(elem_classes="results-container"):
            with gr.Column(scale=2):
                with gr.Column(elem_classes="answer-box"):
                    answer_output = gr.Markdown(elem_classes="markdown-content")
                with gr.Accordion("Chat History", open=False, elem_classes="accordion"):
                    chat_history_display = gr.Chatbot(elem_classes="chat-history")
            with gr.Column(scale=1):
                with gr.Column(elem_classes="sources-box"):
                    gr.Markdown("### Sources")
                    sources_output = gr.HTML()
        
        with gr.Row(elem_classes="examples-container"):
            gr.Examples(
                examples=[
                    "What are the latest developments in quantum computing?",
                    "Explain the impact of AI on healthcare",
                    "What are the best practices for sustainable living?",
                    "How is climate change affecting ocean ecosystems?"
                ],
                inputs=search_input,
                label="Try these examples"
            )

    # Handle interactions
    search_btn.click(
        fn=process_query,
        inputs=[search_input, chat_history],
        outputs=[answer_output, sources_output, search_btn, chat_history_display]
    )
    
    # Also trigger search on Enter key
    search_input.submit(
        fn=process_query,
        inputs=[search_input, chat_history],
        outputs=[answer_output, sources_output, search_btn, chat_history_display]
    )

if __name__ == "__main__":
    demo.launch(share=True)