Update app.py

app.py

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+import streamlit as st
+from PIL import Image
+import requests
+from transformers import BlipProcessor, BlipForConditionalGeneration
+
+# Load the BLIP model
+processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
+model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large")
+
+# Streamlit app
+st.title("Image Captioning with BLIP")
+
+# Uploading the image
+uploaded_image = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+if uploaded_image is not None:
+    image = Image.open(uploaded_image).convert('RGB')
+    st.image(image, caption='Uploaded Image', use_column_width=True)
+
+    # Perform conditional image captioning
+    captioning_mode = st.selectbox("Captioning Mode", ["Conditional", "Unconditional"])
+    if captioning_mode == "Conditional":
+        text = st.text_input("Provide a condition for the captioning (e.g., 'a photo of', 'an illustration of'): ", "a photo of")
+        if text:  # Only proceed if the user has provided a text
+            inputs = processor(image, text, return_tensors="pt")
+            out = model.generate(**inputs)
+            caption = processor.decode(out[0], skip_special_tokens=True)
+            st.write(f"Generated Caption: {caption}")
+    else:  # Unconditional captioning
+        inputs = processor(image, return_tensors="pt")
+        out = model.generate(**inputs)
+        caption = processor.decode(out[0], skip_special_tokens=True)
+        st.write(f"Generated Caption: {caption}")