Create app.py

app.py

@@ -0,0 +1,35 @@
+import streamlit as st
+import requests
+from PIL import Image
+from transformers import BlipProcessor, BlipForConditionalGeneration
+
+# Load model and processor
+def load_model():
+    processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
+    model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
+    return processor, model
+
+processor, model = load_model()
+
+st.title("Image Captioning with BLIP")
+
+uploaded_file = st.file_uploader("Upload an Image", type=["jpg", "png", "jpeg"])
+text_prompt = st.text_input("Enter a prompt for conditional captioning", "here...")
+
+if uploaded_file is not None:
+    image = Image.open(uploaded_file).convert("RGB")
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+    
+    # Conditional captioning
+    inputs = processor(image, text_prompt, return_tensors="pt")
+    out = model.generate(**inputs)
+    conditional_caption = processor.decode(out[0], skip_special_tokens=True)
+    
+    # Unconditional captioning
+    inputs = processor(image, return_tensors="pt")
+    out = model.generate(**inputs)
+    unconditional_caption = processor.decode(out[0], skip_special_tokens=True)
+    
+    st.subheader("Generated Captions")
+    st.write(f"**Conditional Caption:** {conditional_caption}")
+    st.write(f"**Unconditional Caption:** {unconditional_caption}")