Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+import requests
+from dotenv import load_dotenv
+import os
+
+# Load environment variables
+load_dotenv()
+
+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 enrich_classification_with_notion(classification_result):
+    notion_data = get_notion_entries()
+    enriched_results = []
+    for result in classification_result:
+        condition_name = result['label']
+        for entry in notion_data['results']:
+            if entry['properties']['condition_name']['title'][0]['text']['content'] == condition_name:
+                enriched_result = {
+                    "label": condition_name,
+                    "score": result['score'],
+                    "condition_full_name": entry['properties']['condition_full_name']['rich_text'][0]['text']['content'],
+                    "villain": entry['properties']['villain']['rich_text'][0]['text']['content'],
+                    "hero": entry['properties']['hero']['rich_text'][0]['text']['content'],
+                    "hero_image": entry['properties']['hero_image']['url'],
+                    "product_url": entry['properties']['product_url']['url']
+                }
+                enriched_results.append(enriched_result)
+    return enriched_results
+
+# Initialize the image classification pipeline
+classifier = pipeline("image-classification", model="ahishamm/vit-base-HAM-10000-patch-32")
+
+def classify_image(image):
+    results = classifier(image)
+    enriched_results = enrich_classification_with_notion(results)
+    return enriched_results
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=classify_image,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.JSON(),
+    title="Skin Condition Classifier",
+    description="Upload an image to classify the skin condition and get enriched data from Notion."
+)
+
+iface.launch()