done

.gitattributes

@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.sav filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,56 @@
+import streamlit as st
+import requests
+import pickle
+from PIL import Image
+from transformers import BlipProcessor
+with st.sidebar:
+    st.subheader('Image Captioning App using BLIP')
+    st.write('This app uses the BLIP model to generate captions for images.Model card for image captioning pretrained on COCO dataset - base architecture (with ViT base backbone).')
+    image = Image.open('details.png')
+    st.image(image, caption='BLIP Model')
+    st.code('App Built by Ambuj Raj', language='python')
+
+
+st.title('Image Captioning App using BLIP')
+
+flag_image=0
+flag_url=0
+tab1, tab2 = st.tabs(["Upload Image", "Use URL"])
+with tab1:
+    flag_image=1
+    uploaded_file = st.file_uploader("Choose a image",type=['png','jpeg','jpg'])
+    if uploaded_file is not None:
+        st.image(uploaded_file, width=300)
+        raw_image = Image.open(uploaded_file).convert('RGB')
+
+with tab2:
+    flag_url=1
+    img_url = st.text_input('Enter URL of image')
+    if img_url:
+        raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')
+        st.image(raw_image, width=300)
+
+if st.button('Generate Caption'):
+    if(flag_image==1):
+        flag_image=1
+        with st.spinner('Generating Caption...'):
+            processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
+            filename = 'blip.sav'
+            loaded_model = pickle.load(open(filename, 'rb'))
+        inputs = processor(raw_image, return_tensors="pt")
+        out = loaded_model.generate(**inputs)
+        st.success('Caption Generated!')
+        st.write('Generated Caption is: ',processor.decode(out[0], skip_special_tokens=True))
+    elif(flag_url==1):
+        flag_url=0
+        with st.spinner('Generating Caption...'):
+            processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
+            filename = 'blip.sav'
+            loaded_model = pickle.load(open(filename, 'rb'))
+        inputs = processor(raw_image, return_tensors="pt")
+        out = loaded_model.generate(**inputs)
+        st.success('Caption Generated!')
+        st.write('Generated Caption is: ',processor.decode(out[0], skip_special_tokens=True))
+
+
+

blip.sav

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d080c8a428adc06c30862e46cae29cb24c6aadb4e36bd53572798cc1c1f152b7
+size 989879618

requirements.txt

@@ -0,0 +1,3 @@
+streamlit
+transformers
+torch