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import streamlit as st
import pandas as pd
import requests
# Function to fetch images from the API
@st.cache_data
def fetch_images(postID):
try:
response = requests.get(f'https://civitai.com/api/v1/images?postId={postID}')
response.raise_for_status() # This will raise an exception for HTTP errors
data = response.json()
items = data["items"]
table_data = [[item["id"], item["meta"]["prompt"], item["meta"].get("negativePrompt", "N/A"), item["meta"]["Model"],item["meta"]["sampler"], item["meta"]["steps"], item["meta"]["cfgScale"], item["meta"].get("Size", "missing"), item["meta"].get("width", "missing"), item["meta"].get("height", "missing"), item["url"]] for item in items]
return table_data
except requests.RequestException as e:
st.error(f"Failed to fetch images: {e}")
return [] # Return an empty list in case of error
def display_images(df):
if not df.empty:
for _, row in df.iterrows():
st.markdown("---")
col1, col2 = st.columns([1, 2]) # Adjust the ratio as needed
with col1:
st.image(row['url'], use_column_width=True)
with col2:
st.markdown(f"**Prompt:** {row['prompt']}")
st.markdown(f"**Negative Prompt:** {row['negativePrompt']}")
st.markdown(f"**Model:** {row['Model']}")
st.markdown(f"**Sampler:** {row['sampler']} | **Steps:** {row['steps']}")
st.markdown(f"**Config Scale:** {row['cfgScale']}")
st.markdown(f"**Image Size:** {row['Size']} (W x H) OR {row['width']} x {row['height']} (W x H))")
def main():
st.title('Civitai Posts - Prompt Summary')
st.markdown("""
Sample Stable Diffusion posts on Civitai - as of 2024-02-29
- post id 1576676 for [Stable Cascade](https://civitai.com/posts/1576676) - People prompts
- post id 1593777 for [Stable Cascade](https://civitai.com/posts/1593777) - YAML based art prompts
- post id 1593788 for [Stable Cascade](https://civitai.com/posts/1593788) - YAML based life sciences prompts
- post id 1496616 for [DreamShaper Lightning](https://civitai.com/posts/1496616) - Lykon's model showcase
- post id 1515491 for [Juggernaut Lightning](https://civitai.com/posts/1515491) - KandooAI's model showcase
""")
# Using st.query_params to access query parameters
query_params = st.query_params
postID = query_params.get('postID')# Access the 'postID' parameter
postID_input = st.text_input('Enter Post ID:', value=postID) # Prefill the input with the query parameter
if postID_input: # only called if postID_input is not empty
# Convert postID to integer if necessary
try:
postID_int = int(postID_input)
df = pd.DataFrame(fetch_images(postID_int), columns=["id", "prompt", "negativePrompt", "Model", "sampler", "steps", "cfgScale", "Size", "width", "height", "url"])
if not df.empty:
display_images(df)
else:
st.write("No images found for the given Post ID.")
except ValueError:
st.error("Please enter a valid Post ID.")
if __name__ == "__main__":
main()
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