Create app.py

app.py

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+import streamlit as st
+import requests
+from io import BytesIO
+from PIL import Image
+import os
+from transformers import AutoFeatureExtractor, AutoModelForImageClassification
+
+
+api_key = os.environ['API_KEY']
+
+API_URL = "https://api-inference.huggingface.co/models/Hrishikesh332/autotrain-meme-classification-42897109437"
+headers = {"Authorization": f"Bearer {api_key}"}
+
+def query(filename):
+    with open(filename, "rb") as f:
+        data = f.read()
+    response = requests.post(API_URL, headers=headers, data=data)
+    return response.json()
+
+
+
+st.markdown("<h1 style='text-align: center;'>Memeter 💬</h1>", unsafe_allow_html=True)
+st.markdown("---")
+with st.sidebar:
+    st.title("Memometer")
+    st.caption('''
+    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 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:
+    try:
+        image = Image.open(BytesIO(img.read()))
+        output = query(image)
+        st.write("Predicted Output:", Output)
+    except:
+        st.write("Pleas do upload the image in the correct format!")
+    
+