update

0_⛵_GoJourney.py

@@ -0,0 +1,203 @@
+import streamlit as st
+import base64
+import io
+import random
+import time
+from typing import List
+from PIL import Image
+import aiohttp
+import asyncio
+from streamlit_image_select import image_select
+import requests
+import streamlit as st
+import requests
+import zipfile
+import io
+import pandas as pd
+from core import *
+from utils import icon
+from streamlit_image_select import image_select
+from PIL import Image
+import random
+import time
+import base64
+from typing import List
+import aiohttp
+import asyncio
+import plotly.express as px
+from common import set_page_container_style
+
+replicate_text = "NicheImage - Subnet 23 - Bittensor"
+replicate_logo = "assets/NicheTensorTransparent.png"
+replicate_link = "https://github.com/NicheTensor/NicheImage"
+
+st.set_page_config(
+    page_title="NicheImage Generator", page_icon=replicate_logo, layout="wide"
+)
+set_page_container_style(
+    max_width=1100,
+    max_width_100_percent=True,
+    padding_top=0,
+    padding_right=10,
+    padding_left=5,
+    padding_bottom=10,
+)
+
+
+def fetch_GoJourney(task_id):
+    endpoint = "https://api.midjourneyapi.xyz/mj/v2/fetch"
+    data = {"task_id": task_id}
+    response = requests.post(endpoint, json=data)
+    return response.json()
+
+
+def get_or_create_eventloop():
+    try:
+        return asyncio.get_event_loop()
+    except RuntimeError as ex:
+        if "There is no current event loop in thread" in str(ex):
+            loop = asyncio.new_event_loop()
+            asyncio.set_event_loop(loop)
+            return asyncio.get_event_loop()
+
+
+# UI configurations
+st.markdown(
+    """<style>
+    #root > div:nth-child(1) > div > div > div > div > section > div {padding-top: 2rem;}
+</style>
+
+""",
+    unsafe_allow_html=True,
+)
+css = """
+<style>
+section.main > div:has(~ footer ) {
+    padding-bottom: 5px;
+}
+</style>
+"""
+st.markdown(css, unsafe_allow_html=True)
+
+# API Tokens and endpoints from `.streamlit/secrets.toml` file
+API_TOKEN = st.secrets["API_TOKEN"]
+# Placeholders for images and gallery
+generated_images_placeholder = st.empty()
+gallery_placeholder = st.empty()
+
+
+def configure_sidebar() -> None:
+    """
+    Setup and display the sidebar elements.
+
+    This function configures the sidebar of the Streamlit application,
+    including the form for user inputs and the resources section.
+    """
+    with st.sidebar:
+        st.image(replicate_logo, use_column_width=True)
+        with st.form("my_form"):
+            prompt = st.text_area(
+                ":blue[**Enter prompt ✍🏾**]",
+                value="a beautiful flower under the sun --ar 16:9",
+            )
+            with st.expander(
+                "📚 Advanced",
+                expanded=False,
+            ):
+                uid = st.text_input("Specify an UID", value="-1")
+                secret_key = st.text_input("Enter secret key", value="")
+                seed = st.text_input("Seed", value="-1")
+            # The Big Red "Submit" Button!
+            submitted = st.form_submit_button(
+                "Submit", type="primary", use_container_width=True
+            )
+
+        return (
+            submitted,
+            prompt,
+            uid,
+            secret_key,
+            seed,
+        )
+
+
+def main_midjourney(submitted, prompt, uid, secret_key, seed):
+    data = {
+        "key": "capricorn_feb",
+        "prompt": prompt,
+        "model_name": "GoJourney",
+    }
+    print(data)
+    if submitted:
+        with st.status(
+            "👩🏾‍🍳 Whipping up your words into art...", expanded=True
+        ) as status:
+            try:
+                if submitted:
+                    with generated_images_placeholder.container():
+                        loop = get_or_create_eventloop()
+                        asyncio.set_event_loop(loop)
+                        output = requests.post(
+                            "http://proxy_client_nicheimage.nichetensor.com:10003/generate", json=data
+                        )
+                        output = output.json()
+                        print(output)
+                        task_id = output["task_id"]
+                        task_response = fetch_GoJourney(task_id)
+                        task_status = task_response["status"]
+                        if task_status == "failed":
+                            status.update(label="Task failed", state="error")
+                            return
+                        while True:
+                            task_response = fetch_GoJourney(task_id)
+                            if task_response["status"] == "finished":
+                                status.update(label="Task finished", state="complete")
+                                img_url = task_response["task_result"]["image_url"]
+                                st.image(
+                                    img_url, use_column_width=True, output_format="PNG"
+                                )
+                                st.json(task_response)
+                                break
+                            else:
+                                status.update(
+                                    label=f"Task is still processing - {task_response['status']} - {task_response['meta']['task_request']['process_mode']}",
+                                    state="running",
+                                )
+                            time.sleep(2)
+            except Exception as e:
+                st.error(f"Error: {e}")
+                st.stop()
+
+
+def main():
+    """
+    Main function to run the Streamlit application.
+
+    This function initializes the sidebar configuration and the main page layout.
+    It retrieves the user inputs from the sidebar, and passes them to the main page function.
+    The main page function then generates images based on these inputs.
+    """
+    (
+        submitted,
+        prompt,
+        uid,
+        secret_key,
+        seed,
+    ) = configure_sidebar()
+    main_midjourney(
+        submitted,
+        prompt,
+        uid,
+        secret_key,
+        seed,
+    )
+    if not submitted:
+        with generated_images_placeholder.container():
+            st.image(
+                "https://img.midjourneyapi.xyz/mj/a4a88dfe-4e68-4ff3-8ab1-85a4c2ee5792.png",
+                use_column_width=True,
+            )
+
+
+if __name__ == "__main__":
+    main()

0_📊_Home_&_Statistics.py

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+import streamlit as st
+import requests
+import plotly.express as px
+import pandas as pd
+
+st.set_page_config(page_title="NicheImage Studio", layout="wide")
+st.markdown("## :black[Image Generation Studio by NicheImage]")
+replicate_logo = "assets/NicheTensorTransparent.png"
+
+with st.sidebar:
+    st.image(replicate_logo, use_column_width=True)
+st.markdown(
+    """
+    **NicheImage is a decentralized network of image generation models, powered by the Bittensor protocol. Below you find information about the current models on the network.**
+    """,
+    unsafe_allow_html=True,
+)
+response = requests.get(
+    "http://proxy_client_nicheimage.nichetensor.com:10003/get_uid_info"
+)
+if response.status_code == 200:
+    response = response.json()
+    # Plot distribution of models
+    model_distribution = {}
+    for uid, info in response["all_uid_info"].items():
+        model_name = info["model_name"]
+        model_distribution[model_name] = model_distribution.get(model_name, 0) + 1
+    fig = px.pie(
+        values=list(model_distribution.values()),
+        names=list(model_distribution.keys()),
+        title="Model Distribution",
+    )
+    st.plotly_chart(fig)
+    transformed_dict = []
+    for k, v in response["all_uid_info"].items():
+        transformed_dict.append(
+            {
+                "uid": k,
+                "model_name": v["model_name"],
+                "mean_score": (
+                    sum(v["scores"]) / (len(v["scores"])) if len(v["scores"]) > 0 else 0
+                ),
+            }
+        )
+    transformed_dict = pd.DataFrame(transformed_dict)
+    # plot N bar chart for N models, sorted by mean score
+    for model in model_distribution.keys():
+        model_data = transformed_dict[transformed_dict["model_name"] == model]
+        model_data = model_data.sort_values(by="mean_score", ascending=False)
+        if model_data.mean_score.sum() == 0:
+            continue
+        st.write(f"Model: {model}")
+        st.bar_chart(model_data[["uid", "mean_score"]].set_index("uid"))
+
+else:
+    st.error("Error getting miner info")

requirements.txt

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+replicate
+streamlit==1.29
+requests
+streamlit-image-select
+plotly
+pandas
+httpx
+aiohttp