Update app.py

app.py

@@ -1,48 +1,49 @@
-# import gradio as gr
-# # from PIL import Image
-# from transformers.utils import logging
-# from transformers import BlipForConditionalGeneration, AutoProcessor
-
-# logging.set_verbosity_error()
+import gradio as gr
+# from PIL import Image
+from transformers.utils import logging
+from transformers import BlipForConditionalGeneration, AutoProcessor
 
-# model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
-# processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
+logging.set_verbosity_error()
 
-# def caption_image(image):
-#     inputs = processor(image, return_tensors="pt")
-#     out = model.generate(**inputs)
-#     caption = processor.decode(out[0], skip_special_tokens=True)
-#     return caption
+model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
+processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
 
+def caption_image(image):
+    inputs = processor(image, return_tensors="pt")
+    out = model.generate(**inputs)
+    caption = processor.decode(out[0], skip_special_tokens=True)
+    return caption
 
 
+iface = gr.Interface(fn=caption_image, inputs=["image"], outputs="textbox")
+iface.launch()
 # gr.Interface(caption_image, gr.inputs.Image(), "text").launch()
-# # gr.Interface(caption_image, image_input, caption_output).launch()
+# gr.Interface(caption_image, image_input, caption_output).launch()
 
 
 
 
-import streamlit as st
-# from PIL import Image
-from transformers.utils import logging
-from transformers import BlipForConditionalGeneration, AutoProcessor
-import torch
+# import streamlit as st
+# # from PIL import Image
+# from transformers.utils import logging
+# from transformers import BlipForConditionalGeneration, AutoProcessor
+# import torch
 
-logging.set_verbosity_error()
+# logging.set_verbosity_error()
 
-model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
-processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
+# model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
+# processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
 
-st.title("Image Captioning")
+# st.title("Image Captioning")
 
-uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+# uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
 
-if uploaded_file is not None:
-    image = Image.open(uploaded_file)
-    st.image(image, caption="Uploaded Image", use_column_width=True)
-    st.write("")
-    st.write("Generating caption...")
-    inputs = processor(image, return_tensors="pt")
-    out = model.generate(**inputs)
-    caption = processor.decode(out[0], skip_special_tokens=True)
-    st.write("Caption:", caption)
+# if uploaded_file is not None:
+#     image = Image.open(uploaded_file)
+#     st.image(image, caption="Uploaded Image", use_column_width=True)
+#     st.write("")
+#     st.write("Generating caption...")
+#     inputs = processor(image, return_tensors="pt")
+#     out = model.generate(**inputs)
+#     caption = processor.decode(out[0], skip_special_tokens=True)
+#     st.write("Caption:", caption)