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app.py

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+import streamlit as st
+from tensorflow.keras.applications.resnet50 import ResNet50
+from tensorflow.keras.preprocessing import image
+from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions
+import numpy as np
+
+st.title("Image Classification with ResNet50 :baby:")
+
+uploaded_file = st.file_uploader("Upload an image on a object,animal,plant etc.", type=["jpg", "jpeg","png"])
+
+if uploaded_file is not None:
+    img = image.load_img(uploaded_file, target_size=(224, 224))
+    img = image.img_to_array(img)
+    img = np.expand_dims(img, axis=0)
+    img = preprocess_input(img)
+
+    model = ResNet50(weights='imagenet')
+    pred = model.predict(img)
+    decoded_pred = decode_predictions(pred, top=3)[0]
+
+    st.image(uploaded_file, caption='Uploaded Image', use_column_width=True)
+
+    sentence = "This image is "
+    for i, (code, name, probability) in enumerate(decoded_pred):
+        if i == 0:
+            top_name = name.lower()
+            sentence += f"{probability * 100:.2f}% a {top_name}"
+        else:
+            sentence += f", {probability * 100:.2f}% a {name.lower()}"
+    sentence += "."
+    st.markdown(f"<h1>{top_name.upper()}</h1>", unsafe_allow_html=True)
+    st.write(sentence)

repuirements.txt

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+streamlit
+tensorflow
+numpy