Updated app.py

app.py

@@ -1,38 +1,28 @@
-import requests
-from io import BytesIO
+# app.py
+
+import streamlit as st
+from transformers import pipeline
 from PIL import Image
-import os
+import requests
+
+# Load the Hugging Face pipeline for image classification
+classifier = pipeline("image-classification", model="imfarzanansari/skintelligent-acne")
 
-def get_acne_classification(image_input):
-    # Hugging Face Spaces URL
-    url = "https://huggingface.co/spaces/francoisMav/skin_acne"  # Update with your actual Space URL
+# Title of the Streamlit app
+st.title("Skin Condition Classification - Acne Detection")
 
-    # Check if the input is a URL or a local file
-    if image_input.startswith('http://') or image_input.startswith('https://'):
-        # If it's a URL, download the image
-        response = requests.get(image_input)
-        if response.status_code == 200:
-            # Load the image into memory using PIL
-            image = Image.open(BytesIO(response.content))
-        else:
-            return {"error": "Failed to download image from URL"}
-    elif os.path.isfile(image_input):
-        # If it's a file path, open the image
-        image = Image.open(image_input)
-    else:
-        return {"error": "Invalid image input. Must be a URL or valid file path."}
+# Upload an image file
+uploaded_file = st.file_uploader("Choose an image...", type="jpg")
 
-    # Convert image to the format needed for Hugging Face Spaces
-    buffered = BytesIO()
-    image.save(buffered, format="JPEG")
-    buffered.seek(0)
+if uploaded_file is not None:
+    # Display the uploaded image
+    image = Image.open(uploaded_file)
+    st.image(image, caption="Uploaded Image", use_column_width=True)
 
-    # Send the image to the Hugging Face Space via POST request
-    files = {'file': buffered}
-    response = requests.post(url, files=files)
+    # Perform image classification using the Hugging Face pipeline
+    st.write("Classifying...")
+    predictions = classifier(image)
 
-    # Check the response and return the prediction
-    if response.status_code == 200:
-        return response.json()  # Adjust based on how your Space returns predictions
-    else:
-        return {"error": "Failed to get prediction from Hugging Face Space"}
+    # Display the results
+    for pred in predictions:
+        st.write(f"Label: {pred['label']}, Score: {pred['score']:.4f}")