Update app.py

app.py

@@ -1,4 +1,6 @@
 import streamlit as st
+import requests
+from io import BytesIO
 from PIL import Image
 from transformers import AutoFeatureExtractor, AutoModelForImageClassification
 
@@ -7,6 +9,9 @@ def load_image(img):
     return im
 size=20
 
+extractor = AutoFeatureExtractor.from_pretrained("Hrishikesh332/autotrain-meme-classification-42897109437")
+model = AutoModelForImageClassification.from_pretrained("Hrishikesh332/autotrain-meme-classification-42897109437")
+
 st.markdown("<h1 style='text-align: center;'>Memeter 💬</h1>", unsafe_allow_html=True)
 st.markdown("---")
 with st.sidebar:
@@ -15,12 +20,21 @@ with st.sidebar:
     Memeter is an application used for the classification of whether the images provided is meme or not meme
     ''', unsafe_allow_html=False)
 
-img = st.file_uploader("Choose a Image:")
+img = st.file_uploader("Choose an image", type=["jpg", "jpeg", "png"])
+
+def predict(image):
+    inputs = extractor(images=image, return_tensors="pt")
+
+    outputs = model(**inputs)
+    scores = outputs.logits.detach().numpy()
+    return scores
+    
 if img is not None:
-    st.write(img)
-    extractor = AutoFeatureExtractor.from_pretrained("Hrishikesh332/autotrain-meme-classification-42897109437")
-    model = AutoModelForImageClassification.from_pretrained("Hrishikesh332/autotrain-meme-classification-42897109437")
-    pred=model(img)
-    st.code(pred)
+    try:
+        image = Image.open(BytesIO(img.read()))
+        s = predict(image)
+        st.write("Value:", s)
+    except:
+        st.write("Pleas do upload the image in the correct format!")