app.py

import streamlit as st
import streamlit.components.v1 as components
from transformers import pipeline
from diffusers import StableDiffusionPipeline
from datasets import load_dataset
from peft import PeftConfig
from accelerate import Accelerator
from optimum.onnxruntime import ORTModelForSequenceClassification
import torch
import time

# Cache resource-intensive models
@st.cache_resource
def load_diffuser_model():
    return StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")

# Sidebar navigation
st.sidebar.title("🤗 Libraries Demo")
st.sidebar.markdown("Explore text 📝, images 🖼️, and model ops 🔗 with Hugging Face and Arcee!")
page = st.sidebar.selectbox(
    "Choose a Section",
    [
        "🏠 Home",
        "🔄 Workflow",
        "📝 Transformers",
        "🖼️ Diffusers",
        "📊 Datasets",
        "⚙️ PEFT",
        "🚀 Accelerate",
        "⚡ Optimum",
        "📚 DistillKit",
        "🔗 MergeKit",
        "❄️ Spectrum"
    ],
    help="Select a library to explore!"
)

# Mermaid graph for DistillKit, MergeKit, and Spectrum workflows
mermaid_code = """
graph TD
    subgraph DistillKit
        A1[Load Teacher Model] --> B1[Load Student Model]
        B1 --> C1[Configure Distillation]
        C1 --> D1[Perform Distillation]
        D1 --> E1[Evaluate Model]
    end
    subgraph MergeKit
        A2[Select Models] --> B2[Choose Merge Method]
        B2 --> C2[Set Parameters]
        C2 --> D2[Merge Models]
        D2 --> E2[Save Merged Model]
    end
    subgraph Spectrum
        A3[Load Model] --> B3[Analyze Layers]
        B3 --> C3[Generate Config]
        C3 --> D3[Apply Freezing]
        D3 --> E3[Train/Evaluate Model]
    end
"""

# Home Page
if page == "🏠 Home":
    st.title("Hugging Face & Arcee Libraries Demo 🌟")
    st.markdown("""
    Welcome to an interactive demo of powerful libraries for text, image, and model processing!  
    - **📝 Text**: Analyze or generate text with Transformers.  
    - **🖼️ Images**: Create visuals with Diffusers.  
    - **🔗 Models**: Distill, merge, and optimize with Arcee's DistillKit, MergeKit, and Spectrum.  
    Navigate via the sidebar to explore each library!
    """)

# Workflow Page with Mermaid Graph
elif page == "🔄 Workflow":
    st.header("🔄 Workflows: DistillKit, MergeKit, Spectrum")
    st.markdown("See how inputs flow to outputs in Arcee’s libraries with this Mermaid graph:")
    components.html(f"""
    <div id="mermaid"></div>
    <script src="https://cdn.jsdelivr.net/npm/mermaid/dist/mermaid.min.js"></script>
    <script>
        mermaid.initialize({{ startOnLoad: true }});
        const mermaidCode = `{mermaid_code}`;
        mermaid.render('graph', mermaidCode, (svgCode) => {{
            document.getElementById('mermaid').innerHTML = svgCode;
        }});
    </script>
    """, height=600)

# Transformers Section
elif page == "📝 Transformers":
    st.header("📝 Transformers")
    st.markdown("Process text with pre-trained models.")
    task = st.selectbox("Task", ["Sentiment Analysis", "Text Generation"])
    text = st.text_area("Input Text", "")
    if st.button("Run") and text:
        with st.spinner("Processing..."):
            if task == "Sentiment Analysis":
                result = pipeline("sentiment-analysis")(text)
                st.write(f"Result: {result[0]['label']} (Score: {result[0]['score']:.2f})")
            else:
                result = pipeline("text-generation")(text, max_length=50)[0]['generated_text']
                st.write(f"Generated: {result}")

# Diffusers Section
elif page == "🖼️ Diffusers":
    st.header("🖼️ Diffusers")
    st.markdown("Generate images from text.")
    prompt = st.text_input("Prompt", "A futuristic city")
    if st.button("Generate"):
        with st.spinner("Generating..."):
            pipe = load_diffuser_model()
            image = pipe(prompt).images[0]
            st.image(image, caption=prompt)

# Datasets Section
elif page == "📊 Datasets":
    st.header("📊 Datasets")
    st.markdown("Load and explore datasets.")
    dataset = st.selectbox("Dataset", ["imdb", "squad"])
    if st.button("Load"):
        data = load_dataset(dataset, split="train[:5]")
        st.write(data)

# PEFT Section
elif page == "⚙️ PEFT":
    st.header("⚙️ PEFT")
    st.markdown("Parameter-efficient fine-tuning.")
    text = st.text_area("Text", "")
    if st.button("Classify") and text:
        st.write("Simulated PEFT classification: Positive")

# Accelerate Section
elif page == "🚀 Accelerate":
    st.header("🚀 Accelerate")
    st.markdown("Optimize across devices.")
    text = st.text_area("Text", "")
    if st.button("Analyze") and text:
        accelerator = Accelerator()
        result = pipeline("sentiment-analysis")(text)
        st.write(f"Result: {result[0]['label']} (Score: {result[0]['score']:.2f})")

# Optimum Section
elif page == "⚡ Optimum":
    st.header("⚡ Optimum")
    st.markdown("Hardware-accelerated inference.")
    text = st.text_area("Text", "")
    if st.button("Classify") and text:
        st.write("Simulated Optimum result: Positive")

# DistillKit Section
elif page == "📚 DistillKit":
    st.header("📚 DistillKit: Model Distillation")
    st.markdown("Distill large models into smaller, efficient ones. Here are the top 5 functions:")

    # 1. Load teacher model
    teacher = st.selectbox("Teacher Model", ["arcee-ai/Arcee-Spark", "bert-base-uncased"])
    st.write(f"1. Loaded teacher: {teacher}")

    # 2. Load student model
    student = st.selectbox("Student Model", ["Qwen/Qwen2-1.5B", "distilbert-base-uncased"])
    st.write(f"2. Loaded student: {student}")

    # 3. Configure distillation
    temp = st.slider("Temperature", 1.0, 5.0, 2.0)
    alpha = st.slider("Alpha", 0.0, 1.0, 0.5)
    st.write(f"3. Config: Temp={temp}, Alpha={alpha}")

    # 4. Perform distillation (simulated)
    if st.button("Distill"):
        with st.spinner("Distilling..."):
            time.sleep(2)
            st.success("4. Distillation complete!")

        # 5. Evaluate distilled model
        st.write("5. Evaluating...")
        metrics = {"accuracy": 0.85, "loss": 0.12}
        st.write(f"Metrics: {metrics}")

    st.markdown("""
    **How It Works:**  
    DistillKit compresses a teacher model into a student model using distillation techniques.  
    ```python
    config = {"teacher": "arcee-ai/Arcee-Spark", "student": "Qwen/Qwen2-1.5B", "temp": 2.0, "alpha": 0.5}