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945c098 f21b3ed 945c098 f21b3ed 945c098 f21b3ed 945c098 f21b3ed 945c098 f21b3ed 945c098 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 | import requests
import pandas as pd
import os
import gradio as gr
from transformers import pipeline
# Load environment variables
NOTION_TOKEN = os.getenv('NOTION_TOKEN')
DATABASE_ID = os.getenv('DATABASE_ID')
headers = {
"Authorization": f"Bearer {NOTION_TOKEN}",
"Content-Type": "application/json",
"Notion-Version": "2022-06-28"
}
def get_notion_entries():
url = f"https://api.notion.com/v1/databases/{DATABASE_ID}/query"
response = requests.post(url, headers=headers)
response.raise_for_status() # Raise an exception for HTTP errors
return response.json()
def get_full_text(property):
if property and 'rich_text' in property and property['rich_text']:
return ''.join([text_part['text']['content'] for text_part in property['rich_text']])
elif property and 'title' in property and property['title']:
return ''.join([text_part['text']['content'] for text_part in property['title']])
return ""
def notion_to_dataframe(notion_data):
# Prepare lists to create DataFrame
condition_names = []
condition_full_names = []
villains = []
heroes = []
hero_images = []
product_urls = []
# Iterate through Notion entries and collect data
for entry in notion_data['results']:
condition_names.append(get_full_text(entry['properties']['condition_name']))
condition_full_names.append(get_full_text(entry['properties']['condition_full_name']))
villains.append(get_full_text(entry['properties']['villain']))
heroes.append(get_full_text(entry['properties']['hero']))
hero_images.append(entry['properties']['hero_image']['url'] if entry['properties']['hero_image'] else None)
product_urls.append(entry['properties']['product_url']['url'] if entry['properties']['product_url'] else None)
# Create DataFrame
df = pd.DataFrame({
'Condition Name': condition_names,
'Condition Full Name': condition_full_names,
'Villain': villains,
'Hero': heroes,
'Hero Image': hero_images,
'Product URL': product_urls
})
return df
# Fetch data
notion_data = get_notion_entries()
# Convert to DataFrame
df = notion_to_dataframe(notion_data)
# Initialize the image classification pipeline
classifier = pipeline("image-classification", model="ahishamm/vit-base-HAM-10000-patch-32")
def classify_image(image):
results = classifier(image)
condition_name = results[0]['label']
condition_data = df[df['Condition Name'] == condition_name].iloc[0]
classification = results[0]
confidence = round(classification['score'] * 100, 2)
condition_full_name = condition_data['Condition Full Name']
villain = condition_data['Villain']
hero = condition_data['Hero']
hero_image = condition_data['Hero Image']
product_url = condition_data['Product URL']
enriched_output = f"""
**{condition_full_name} ({confidence}% confident)**
{villain}
{hero} Find out if he is also your hero!
[Learn More]({product_url})
"""
return enriched_output
# Create the Gradio interface
iface = gr.Interface(
fn=classify_image,
inputs=gr.Image(type="pil"),
outputs=gr.Markdown(),
title="Skin Condition Classifier",
description="Upload an image to classify the skin condition and get enriched data from Notion."
)
iface.launch() |