Create app.py

app.py

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+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}