Create app.py

app.py

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+import streamlit as st
+from PIL import Image
+import torch
+from transformers import pipeline
+
+# Initialize the Hugging Face model for image captioning
+image_captioning_model = pipeline("image-captioning")
+
+# Streamlit app title and description
+st.title("Image Information Extractor")
+st.write("Upload an image, and this app will extract information from it.")
+
+# Upload image
+image = st.file_uploader("Upload an image", type=["jpg", "png", "jpeg"])
+
+if image:
+    # Display the uploaded image
+    st.image(image, use_column_width=True, caption="Uploaded Image")
+
+    # Process the image and extract information
+    image = Image.open(image)
+    st.write("Extracting information from the image...")
+
+    # Use the image captioning model to generate a description
+    captions = image_captioning_model(image)
+
+    # Display the image description
+    st.write("Image Description:")
+    for caption in captions:
+        st.write(caption["caption"])
+
+    # You can add more processing or explanations here if needed
+
+st.sidebar.text("Built with ❤️ by Your Moneeb")