Create app.py

app.py

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+import streamlit as st
+from PIL import Image
+from transformers import BlipProcessor, BlipForConditionalGeneration
+import torch
+
+# Load model and processor
+@st.cache_resource
+def load_model():
+    model_name = "your-huggingface-username/your-model-name"
+    processor = BlipProcessor.from_pretrained(model_name)
+    model = BlipForConditionalGeneration.from_pretrained(model_name)
+    return processor, model
+
+processor, model = load_model()
+
+# Streamlit UI
+st.title("Cartoon Caption Generator 🖼️📜")
+st.write("Upload a cartoon image and get a funny caption!")
+
+uploaded_file = st.file_uploader("Upload a Cartoon Image", type=["jpg", "png", "jpeg"])
+
+if uploaded_file is not None:
+    image = Image.open(uploaded_file).convert("RGB")
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+
+    # Preprocess and generate caption
+    inputs = processor(images=image, return_tensors="pt")
+    with torch.no_grad():
+        generated_ids = model.generate(**inputs)
+    caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+
+    st.subheader("Generated Caption:")
+    st.write(f"💬 {caption}")