Create app.py

app.py

@@ -0,0 +1,70 @@
+import streamlit as st
+from PIL import Image
+#import torch
+#from transformers import BlipProcessor, BlipForConditionalGeneration
+
+def set_page_config():
+    st.set_page_config(
+        page_title='Caption an Image', 
+        page_icon=':camera:', 
+        layout='wide',
+    )
+
+#def initialize_model():
+#    hf_model = "Salesforce/blip-image-captioning-large"
+#    device = 'cuda' if torch.cuda.is_available() else 'cpu'
+#    processor = BlipProcessor.from_pretrained(hf_model)
+#    model = BlipForConditionalGeneration.from_pretrained(hf_model).to(device) # type: ignore
+ #   return processor, model, device
+
+def upload_image():
+    return st.sidebar.file_uploader("Upload an image (we aren't storing anything)", type=["jpg", "jpeg", "png"])
+
+def resize_image(image, max_width):
+    width, height = image.size
+    if width > max_width:
+        ratio = max_width / width
+        height = int(height * ratio)
+        image = image.resize((max_width, height))
+    return image
+
+def generate_caption(processor, model, device, image):
+    #inputs = processor(image, return_tensors='pt').to(device)
+    #out = model.generate(**inputs, max_new_tokens=20)
+    #caption = processor.decode(out[0], skip_special_tokens=True)
+    caption="im here "
+    return caption
+
+def main():
+    set_page_config()
+    st.header("Caption an Image :camera:")
+
+    uploaded_image = upload_image()
+
+    if uploaded_image is not None:
+        image = Image.open(uploaded_image)
+        image = resize_image(image, max_width=300)
+
+        st.image(image, caption='Your image')
+        
+        with st.sidebar:
+            st.divider() 
+            if st.sidebar.button('Generate Caption'):
+                with st.spinner('Generating caption...'):
+                    #processor, model, device = initialize_model()
+                    #caption = generate_caption(processor, model, device, image)
+                    caption="im here man"
+                    st.header("Caption:")
+                    st.markdown(f'**{caption}**')
+
+
+
+if __name__ == '__main__':
+    main()
+
+
+st.markdown("""
+    ---
+   You are looking at Finetuned image Caption model """)
+
+