Add Streamlit Demo Template

README.md

@@ -214,6 +214,7 @@ To add a new template/resource:
 - [Akash Trainer](akash-trainer)
 - [Gradio](gradio-demo)
 - [Ray Cluster](ray)
+- [Streamlit](https://github.com/akash-network/awesome-akash/tree/master/streamlit-demo)
 - [Jupyter Notebook](jupyter)
 - [Jupyter Notebook with ezkl](tensorflow-jupyter-ezkl)
 - [Jupyter Notebook with Python Kernel](tensorflow-jupyter-mnist)

streamlit-demo/README.md

@@ -0,0 +1,144 @@
+# Streamlit Demo on Akash
+
+[![Deploy on Akash](https://raw.githubusercontent.com/akash-network/console/refs/heads/main/apps/deploy-web/public/images/deploy-with-akash-btn.svg)](https://console.akash.network/new-deployment?step=edit-deployment&templateId=akash-network-awesome-akash-streamlit-demo)
+<img width="2921" height="811" alt="streamlit-logo-primary-colormark-darktext" src="https://github.com/user-attachments/assets/f10e1237-8993-4f66-97f6-3ad3f7862578" />
+
+Deploy interactive machine learning apps and data dashboards using Streamlit on Akash Network's decentralized cloud.
+
+## What is Streamlit?
+
+[Streamlit](https://streamlit.io) is an open-source Python library that lets you turn data scripts into shareable web apps in minutes — with no frontend experience required. It's the go-to tool for ML engineers and data scientists who want to:
+
+- Demo machine learning models interactively
+- Build data visualization dashboards
+- Prototype and share AI-powered tools
+- Create internal apps without writing HTML/CSS/JS
+
+## What's Included
+
+This template deploys a simple Streamlit application with two demo interfaces:
+
+1. **Text Analysis** — Mock sentiment analysis with confidence scoring
+2. **Image Captioning** — Mock image description generator with file upload
+
+You can easily swap in your own models or logic by editing `app.py`.
+
+## Deployment
+
+### Deploy on Akash Console (Recommended)
+
+1. Go to [console.akash.network](https://console.akash.network)
+2. Click **Deploy** → **Build Your Template**
+3. Upload the `deploy.yaml` file or use the one-click button above
+4. Review resources and set your pricing bid
+5. Sign and deploy
+6. Access your Streamlit app at the provided URL on port 80
+
+### Deploy via Akash CLI
+
+1. **Clone the repository:**
+
+```bash
+git clone https://github.com/akash-network/awesome-akash.git
+cd awesome-akash/streamlit-demo
+```
+
+2. **Create the deployment:**
+
+```bash
+akash tx deployment create deploy.yaml \
+  --from <your-wallet> \
+  --node https://rpc.akashnet.net:443 \
+  --chain-id akashnet-2
+```
+
+3. **Accept a bid and create a lease**, then fetch your deployment URL from the lease status.
+
+## Customization
+
+### Use Your Own Model
+
+Replace the demo functions in `app.py` with real ML logic. Example using HuggingFace Transformers:
+
+```python
+import streamlit as st
+from transformers import pipeline
+
+@st.cache_resource
+def load_model():
+    return pipeline("sentiment-analysis")
+
+classifier = load_model()
+
+st.title("Sentiment Analysis")
+text = st.text_area("Enter text")
+
+if st.button("Analyze"):
+    result = classifier(text)[0]
+    st.success(f"**{result['label']}** — {result['score']:.2%} confidence")
+```
+
+Then build a custom Docker image with your dependencies and update the `image:` field in `deploy.yaml`.
+
+### Adjust Resources
+
+Edit `deploy.yaml` to allocate more CPU or memory for heavier workloads:
+
+```yaml
+resources:
+  cpu:
+    units: 2.0      # Increase for CPU-intensive models
+  memory:
+    size: 4Gi       # Increase for larger models or datasets
+  storage:
+    size: 5Gi       # Increase if loading model weights from disk
+```
+
+### Add GPU Support
+
+To run GPU-accelerated models, add a GPU resource block and use a CUDA-enabled base image:
+
+```yaml
+resources:
+  cpu:
+    units: 4.0
+  memory:
+    size: 16Gi
+  storage:
+    size: 20Gi
+  gpu:
+    units: 1
+    attributes:
+      vendor:
+        nvidia:
+          - model: rtx3090
+```
+
+## Port Reference
+
+| Service   | Container Port | Exposed As |
+|-----------|---------------|------------|
+| Streamlit | 8501          | 80 (HTTP)  |
+
+## Cost Estimate
+
+Typical cost on Akash: **~$5–15/month** depending on provider and resource allocation — significantly cheaper than equivalent workloads on AWS, GCP, or Heroku.
+
+## Example Use Cases
+
+- Deploy HuggingFace models for text generation, classification, or translation
+- Build interactive dashboards for data exploration
+- Share ML research demos without provisioning servers
+- Host internal tools accessible from anywhere
+
+## Resources
+
+- [Streamlit Documentation](https://docs.streamlit.io)
+- [Akash Network Documentation](https://docs.akash.network)
+- [Akash Console](https://console.akash.network)
+- [HuggingFace Models](https://huggingface.co/models)
+
+## Support
+
+- Akash Discord: [discord.akash.network](https://discord.akash.network)
+- Streamlit Community: [discuss.streamlit.io](https://discuss.streamlit.io)

streamlit-demo/app.py

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+import streamlit as st
+import random
+import time
+
+st.set_page_config(
+    page_title="Streamlit Demo on Akash",
+    page_icon="🚀",
+    layout="centered"
+)
+
+st.title("Streamlit Demo — Deployed on Akash Network")
+st.markdown(
+    "This is a simple demo showing how to deploy Streamlit apps on Akash's decentralized cloud."
+)
+
+tab1, tab2 = st.tabs(["Text Analysis", "Image Captioning"])
+
+# --- Tab 1: Sentiment Analysis ---
+with tab1:
+    st.subheader("Sentiment Analysis")
+    text_input = st.text_area("Enter text", placeholder="Type something here...")
+
+    if st.button("Analyze", key="text_btn"):
+        if not text_input.strip():
+            st.warning("Please enter some text!")
+        else:
+            with st.spinner("Analyzing..."):
+                time.sleep(0.5)  # simulate processing
+            sentiments = ["Positive 😊", "Negative 😞", "Neutral 😐"]
+            confidence = random.randint(60, 99)
+            result = random.choice(sentiments)
+            st.success(f"**Sentiment:** {result}")
+            st.metric(label="Confidence", value=f"{confidence}%")
+
+# --- Tab 2: Image Captioning ---
+with tab2:
+    st.subheader("Image Captioning")
+    uploaded = st.file_uploader("Upload an image", type=["png", "jpg", "jpeg", "webp"])
+
+    if st.button("Generate Caption", key="img_btn"):
+        if uploaded is None:
+            st.warning("Please upload an image!")
+        else:
+            st.image(uploaded, caption="Uploaded Image", use_column_width=True)
+            with st.spinner("Generating caption..."):
+                time.sleep(0.5)
+            captions = [
+                "A beautiful landscape with mountains",
+                "A person standing in front of a building",
+                "An abstract colorful composition",
+                "A close-up of nature",
+                "A cityscape at sunset",
+            ]
+            st.success(f"**Caption:** {random.choice(captions)}")

streamlit-demo/config.json

@@ -0,0 +1,9 @@
+{
+  "title": "Streamlit Demo",
+  "description": "Deploy interactive machine learning apps and data dashboards using Streamlit on Akash's decentralized cloud.",
+  "category": "Machine Learning",
+  "tags": ["streamlit", "machine-learning", "data-visualization", "python", "ai", "dashboard"],
+  "logoUrl": "https://streamlit.io/images/brand/streamlit-mark-color.svg",
+  "websiteUrl": "https://streamlit.io",
+  "docsUrl": "https://docs.streamlit.io"
+}

streamlit-demo/deploy.yaml

@@ -0,0 +1,81 @@
+---
+version: "2.0"
+
+services:
+  streamlit:
+    image: python:3.11-slim
+    command:
+      - "bash"
+      - "-c"
+    args:
+      - |
+        pip install streamlit --quiet &&
+        cat << 'EOF' > /app/app.py
+        import streamlit as st
+        import random, time
+        st.set_page_config(page_title="Streamlit Demo on Akash", page_icon="🚀")
+        st.title("Streamlit Demo — Deployed on Akash Network")
+        st.markdown("This is a simple demo showing how to deploy Streamlit apps on Akash's decentralized cloud.")
+        tab1, tab2 = st.tabs(["Text Analysis", "Image Captioning"])
+        with tab1:
+            st.subheader("Sentiment Analysis")
+            text_input = st.text_area("Enter text", placeholder="Type something here...")
+            if st.button("Analyze"):
+                if not text_input.strip():
+                    st.warning("Please enter some text!")
+                else:
+                    with st.spinner("Analyzing..."):
+                        time.sleep(0.5)
+                    sentiments = ["Positive 😊", "Negative 😞", "Neutral 😐"]
+                    confidence = random.randint(60, 99)
+                    st.success(f"**Sentiment:** {random.choice(sentiments)}")
+                    st.metric(label="Confidence", value=f"{confidence}%")
+        with tab2:
+            st.subheader("Image Captioning")
+            uploaded = st.file_uploader("Upload an image", type=["png","jpg","jpeg","webp"])
+            if st.button("Generate Caption"):
+                if uploaded is None:
+                    st.warning("Please upload an image!")
+                else:
+                    st.image(uploaded, use_column_width=True)
+                    with st.spinner("Generating caption..."):
+                        time.sleep(0.5)
+                    captions = ["A beautiful landscape with mountains","A person standing in front of a building","An abstract colorful composition","A close-up of nature","A cityscape at sunset"]
+                    st.success(f"**Caption:** {random.choice(captions)}")
+        EOF
+        mkdir -p /root/.streamlit
+        echo '[server]\nheadless = true\naddress = "0.0.0.0"\nport = 8501\nenableCORS = false\nenableXsrfProtection = false' > /root/.streamlit/config.toml
+        streamlit run /app/app.py
+    expose:
+      - port: 8501
+        as: 80
+        to:
+          - global: true
+
+profiles:
+  compute:
+    streamlit:
+      resources:
+        cpu:
+          units: 1.0
+        memory:
+          size: 1Gi
+        storage:
+          size: 1Gi
+  placement:
+    akash:
+      attributes:
+        host: akash
+      signedBy:
+        anyOf:
+          - "akash1365yvmc4s7awdyj3n2sav7xfx76adc6dnmlx63"
+      pricing:
+        streamlit:
+          denom: uakt
+          amount: 10000
+
+deployment:
+  streamlit:
+    akash:
+      profile: streamlit
+      count: 1