app.py

# app.py
import streamlit as st
st.title("JithAI")
st.write("System online and stable.")
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import matplotlib.pyplot as plt
import numpy as np
import time
import re
import base64

# ---- Configuration ----
MODEL_R1 = "deepseek-ai/DeepSeek-R1-0528"
MODEL_V3 = "deepseek-ai/DeepSeek-V3-0324"
APP_NAME = "JithAI"
PRIMARY_COLOR = "#6366F1"  # Modern indigo
SECONDARY_COLOR = "#8B5CF6"  # Vibrant violet
BG_COLOR = "#0F172A"  # Deep space blue
TEXT_COLOR = "#E2E8F0"  # Light gray text
ACCENT_COLOR = "#06D6A0"  # Teal accent

# ---- Custom CSS ----
st.markdown(f"""
<style>
    @import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;600;700&display=swap');
    
    * {{
        font-family: 'Inter', sans-serif;
    }}
    
    body {{
        background-color: {BG_COLOR}; 
        color: {TEXT_COLOR};
    }}
    
    .stApp {{
        background: linear-gradient(135deg, {BG_COLOR}, #1E293B);
        background-size: 400% 400%;
        animation: gradientBG 15s ease infinite;
    }}
    
    @keyframes gradientBG {{
        0% {{ background-position: 0% 50%; }}
        50% {{ background-position: 100% 50%; }}
        100% {{ background-position: 0% 50%; }}
    }}
    
    .header {{
        color: white; 
        text-align: center;
        padding: 1rem 0;
        background: rgba(30, 41, 59, 0.7);
        border-radius: 16px;
        backdrop-filter: blur(10px);
        box-shadow: 0 4px 30px rgba(0, 0, 0, 0.1);
        border: 1px solid rgba(99, 102, 241, 0.3);
        margin-bottom: 2rem;
    }}
    
    .stButton>button {{
        background: linear-gradient(to right, {PRIMARY_COLOR}, {SECONDARY_COLOR});
        color: white !important;
        border: none;
        border-radius: 12px;
        padding: 12px 28px;
        font-weight: 600;
        transition: all 0.3s ease;
        box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
    }}
    
    .stButton>button:hover {{
        transform: translateY(-2px);
        box-shadow: 0 6px 12px rgba(0, 0, 0, 0.15);
    }}
    
    .stTextArea textarea {{
        background-color: rgba(30, 41, 59, 0.7) !important;
        color: {TEXT_COLOR} !important;
        border: 1px solid {SECONDARY_COLOR} !important;
        border-radius: 12px;
        padding: 15px !important;
    }}
    
    .result-box {{
        background: rgba(30, 41, 59, 0.7);
        border-radius: 16px;
        padding: 25px;
        margin-top: 20px;
        backdrop-filter: blur(5px);
        border: 1px solid rgba(139, 92, 246, 0.2);
        box-shadow: 0 4px 30px rgba(0, 0, 0, 0.1);
    }}
    
    .model-card {{
        background: rgba(15, 23, 42, 0.8);
        border-radius: 12px;
        padding: 20px;
        margin-bottom: 20px;
        border-left: 4px solid {ACCENT_COLOR};
    }}
    
    .footer {{
        text-align: center;
        margin-top: 40px;
        color: #94A3B8;
        font-size: 0.9rem;
    }}
    
    .highlight {{
        background: linear-gradient(120deg, rgba{tuple(int(PRIMARY_COLOR.lstrip('#')[i:i+2], 16) for i in (0, 2, 4)}, 0.3), rgba{tuple(int(SECONDARY_COLOR.lstrip('#')[i:i+2], 16) for i in (0, 2, 4)}, 0.3));
        padding: 2px 6px;
        border-radius: 4px;
        font-weight: 600;
    }}
    
    .tab-content {{
        padding: 20px 0;
    }}
    
    .stProgress > div > div > div {{
        background: linear-gradient(to right, {PRIMARY_COLOR}, {SECONDARY_COLOR}) !important;
    }}
</style>
""", unsafe_allow_html=True)

# ---- App Header ----
st.markdown(f"""
<div class="header">
    <h1>{APP_NAME}</h1>
    <p>Advanced Protein Sequence Analysis with DeepSeek AI</p>
</div>
""", unsafe_allow_html=True)

# ---- Model Loading ----
@st.cache_resource(show_spinner=False)
def load_model(model_name):
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")
    return pipeline("text-generation", model=model, tokenizer=tokenizer)

# Initialize session state
if 'r1_model' not in st.session_state:
    st.session_state.r1_model = None
if 'v3_model' not in st.session_state:
    st.session_state.v3_model = None
if 'current_tab' not in st.session_state:
    st.session_state.current_tab = "Analysis"

# ---- Model Cards ----
with st.container():
    col1, col2 = st.columns(2)
    
    with col1:
        st.markdown("""
        <div class="model-card">
            <h3>DeepSeek-R1-0528</h3>
            <p>Advanced 52.8B parameter model for precise protein analysis and functional predictions</p>
            <p><span class="highlight">Specialized</span> in protein sequence interpretation</p>
        </div>
        """, unsafe_allow_html=True)
    
    with col2:
        st.markdown("""
        <div class="model-card">
            <h3>DeepSeek-V3-0324</h3>
            <p>Cutting-edge 32.4B parameter model for generative protein design and sequence optimization</p>
            <p><span class="highlight">Optimized</span> for protein engineering tasks</p>
        </div>
        """, unsafe_allow_html=True)

# ---- Tab Navigation ----
tabs = ["Analysis", "Sequence Generator", "Protein Explorer"]
current_tab = st.radio("", tabs, index=0, horizontal=True, label_visibility="collapsed")

# ---- Input Section ----
protein_seq = st.text_area(
    "Enter Protein Sequence:",
    height=180,
    placeholder="MTAIIKEIVSRNKRRYQEDGFDLDLTYIYPNIIAMGFPAERLEGVYRNNIDDVVRFLDSKHKNHYKIYNLCA...",
    help="Enter amino acid sequence in single-letter code"
)

# ---- Tab Content ----
if current_tab == "Analysis":
    st.markdown("### Protein Analysis")
    analysis_prompt = st.text_input(
        "Analysis Focus (optional):",
        placeholder="e.g., Identify potential binding sites, analyze structural motifs",
        help="Specify what you want to analyze in the protein sequence"
    )
    
    if st.button("Analyze with DeepSeek-R1", use_container_width=True):
        if not protein_seq:
            st.warning("Please input a protein sequence")
        else:
            with st.spinner("Initializing DeepSeek-R1 model..."):
                if not st.session_state.r1_model:
                    st.session_state.r1_model = load_model(MODEL_R1)
            
            with st.spinner("Analyzing protein structure..."):
                prompt = f"""
                [INST] You are an expert bioinformatician specializing in protein analysis. 
                Analyze the following protein sequence and provide detailed insights:
                
                Protein Sequence:
                {protein_seq}
                
                {f"Focus: {analysis_prompt}" if analysis_prompt else ""}
                
                Provide your analysis in the following format:
                1. Structural characteristics
                2. Potential functional domains
                3. Binding site predictions
                4. Stability and solubility assessment
                5. Potential modifications for optimization
                [/INST]
                """
                
                progress_bar = st.progress(0)
                result_container = st.empty()
                full_response = ""
                
                for i in range(1, 101):
                    time.sleep(0.02)
                    progress_bar.progress(i)
                    
                    if i % 20 == 0:
                        # Simulate intermediate results
                        intermediate = f"Analysis in progress... {i}% complete"
                        result_container.markdown(f"""
                        <div class="result-box">
                            <p>{intermediate}</p>
                        </div>
                        """, unsafe_allow_html=True)
                
                # Generate actual response
                response = st.session_state.r1_model(
                    prompt,
                    max_new_tokens=800,
                    temperature=0.7,
                    do_sample=True,
                    top_p=0.9,
                )
                
                # Extract the generated text
                analysis = response[0]['generated_text'].split('[/INST]')[-1].strip()
                
                # Format the analysis with markdown
                formatted_analysis = re.sub(
                    r'(\d+\.\s+[^\n]+)',
                    r'<br><span style="color:#8B5CF6; font-weight:600">\1</span><br>', 
                    analysis
                )
                
                progress_bar.empty()
                st.markdown(f"""
                <div class="result-box">
                    <h3>Analysis Results</h3>
                    <div style="line-height: 1.8; margin-top: 15px;">
                        {formatted_analysis}
                    </div>
                </div>
                """, unsafe_allow_html=True)

elif current_tab == "Sequence Generator":
    st.markdown("### Protein Sequence Generation")
    design_goal = st.text_input(
        "Design Goal:",
        placeholder="e.g., Create a thermostable enzyme for DNA repair",
        help="Describe the protein you want to generate"
    )
    
    if st.button("Generate with DeepSeek-V3", use_container_width=True):
        if not design_goal:
            st.warning("Please enter a design goal")
        else:
            with st.spinner("Initializing DeepSeek-V3 model..."):
                if not st.session_state.v3_model:
                    st.session_state.v3_model = load_model(MODEL_V3)
            
            with st.spinner("Designing optimized protein sequence..."):
                prompt = f"""
                [INST] You are an AI protein engineer. Design a novel protein sequence based on the following requirements:
                
                Design Goal: {design_goal}
                
                Provide:
                1. A novel protein sequence (60-80 amino acids)
                2. Brief explanation of key features
                3. Potential applications
                [/INST]
                """
                
                progress_bar = st.progress(0)
                result_container = st.empty()
                
                for i in range(1, 101):
                    time.sleep(0.02)
                    progress_bar.progress(i)
                    
                response = st.session_state.v3_model(
                    prompt,
                    max_new_tokens=400,
                    temperature=0.8,
                    do_sample=True,
                    top_p=0.95,
                )
                
                # Extract the generated text
                generation = response[0]['generated_text'].split('[/INST]')[-1].strip()
                
                # Extract the protein sequence using regex
                sequence_match = re.search(r'([A-Z]{60,})', generation)
                sequence = sequence_match.group(1) if sequence_match else "Sequence not found"
                
                # Highlight the sequence in the response
                highlighted_generation = generation.replace(
                    sequence, 
                    f'<span style="background: rgba{tuple(int(ACCENT_COLOR.lstrip("#")[i:i+2], 16) for i in (0, 2, 4)}, 0.3); padding: 3px; border-radius: 4px; font-family: monospace;">{sequence}</span>'
                )
                
                progress_bar.empty()
                
                st.markdown(f"""
                <div class="result-box">
                    <h3>Generated Protein</h3>
                    <div style="line-height: 1.8; margin-top: 15px;">
                        {highlighted_generation}
                    </div>
                </div>
                """, unsafe_allow_html=True)
                
                # Sequence visualization
                st.markdown("### Sequence Visualization")
                fig, ax = plt.subplots(figsize=(10, 1.5))
                ax.text(0.5, 0.5, sequence, 
                         fontfamily='monospace', 
                         fontsize=9, 
                         ha='center', 
                         va='center')
                ax.set_xlim(0, 1)
                ax.set_ylim(0, 1)
                ax.axis('off')
                st.pyplot(fig, use_container_width=True)

elif current_tab == "Protein Explorer":
    st.markdown("### Protein Structure Explorer")
    st.info("This module provides interactive visualization of protein structures")
    
    # Protein structure visualization placeholder
    st.image("https://cdn.rcsb.org/images/structures/1mbn/1mbn_assembly-1.jpeg", 
             caption="Protein Structure Visualization", 
             use_column_width=True)
    
    col1, col2 = st.columns(2)
    with col1:
        st.selectbox("Visualization Style", ["Cartoon", "Surface", "Ribbon", "Ball & Stick"])
    with col2:
        st.selectbox("Color Scheme", ["By Element", "By Chain", "By Residue Type", "Hydrophobicity"])
    
    st.slider("Rotation", 0, 360, 45)
    st.button("Render Structure", use_container_width=True)

# ---- Footer ----
st.markdown("---")
st.markdown(f"""
<div class="footer">
    <p>{APP_NAME} v1.2 | Advanced Protein Analysis Platform</p>
    <p style="font-size: 0.85rem; margin-top: 10px;">
    Powered by DeepSeek-R1 and DeepSeek-V3 models | This tool is for research purposes only
    </p>
</div>
""", unsafe_allow_html=True)