first commit

README.md

@@ -1,10 +1,10 @@
 ---
-title: Flyer Segmenter
-emoji: 📈
-colorFrom: red
-colorTo: green
+title: flyer-segmenter
+emoji: 📊
+colorFrom: pink
+colorTo: pink
 sdk: streamlit
-sdk_version: 1.29.0
+sdk_version: 1.28.2
 app_file: app.py
 pinned: false
 ---

app.py

@@ -0,0 +1,41 @@
+import torch
+import streamlit as st
+import numpy as np
+from PIL import Image, ImageDraw
+from transformers import pipeline
+from tempfile import NamedTemporaryFile
+
+#detector = pipeline(model="google/owlvit-base-patch32", task="zero-shot-object-detection")
+detector = pipeline("object-detection", model="sergioska/yolo-finetuned-flyers")
+
+st.title('Upload an image file to detection')
+
+uploaded_image_zero_file = st.file_uploader("Choose an image file (zero)")
+texts = st.text_input('tags')
+
+if uploaded_image_zero_file is not None:
+    image = Image.open(uploaded_image_zero_file)
+    outputImage = np.array(image)
+    predictions = detector(
+        image,
+        candidate_labels=['eggs', 'apple', 'pear']
+    )
+    st.image(outputImage)
+
+if st.button('apply tag'):
+    tags = [['products', 'price', 'title']]
+
+    draw = ImageDraw.Draw(image)
+
+    for prediction in predictions:
+        box = prediction["box"]
+        label = prediction["label"]
+        score = prediction["score"]
+
+        xmin, ymin, xmax, ymax = box.values()
+        draw.rectangle((xmin, ymin, xmax, ymax), outline="red", width=3)
+        draw.text((xmin, ymin), f"{label}: {round(score,5)}", fill="white")
+
+    st.image(image)
+    st.write(predictions)
+        

requirements.txt

@@ -0,0 +1,3 @@
+streamlit
+accelerate
+transformers