Create app.py

app.py

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+import streamlit as st
+from transformers import VisionEncoderDecoderModel, AutoFeatureExtractor, AutoTokenizer
+import requests
+from PIL import Image
+import torch
+
+
+CHECKPOINT = "adalbertojunior/image_captioning_portuguese"
+
+@st.cache
+def get_model():
+    model = VisionEncoderDecoderModel.from_pretrained(CHECKPOINT)
+    return model
+
+
+feature_extractor = AutoFeatureExtractor.from_pretrained(CHECKPOINT)
+tokenizer = AutoTokenizer.from_pretrained(CHECKPOINT)
+
+st.title("Image Captioning with ViT & GPT2 🇧🇷")
+
+st.sidebar.markdown("## Generation parameters")
+max_length = st.sidebar.number_input("Max length", value=20, min_value=1)
+no_repeat_ngram_size = st.sidebar.number_input("no repeat ngrams size", value=2, min_value=1)
+num_return_sequences = st.sidebar.number_input("Generated sequences", value=3, min_value=1)
+
+gen_mode = st.sidebar.selectbox("Generation mode", ["beam search", "sampling"])
+if gen_mode == "beam search": 
+    num_beams = st.sidebar.number_input("Beam size", value=5, min_value=1)
+    early_stopping = st.sidebar.checkbox("Early stopping", value=True)
+    gen_params = {
+        "num_beams": num_beams,
+        "early_stopping": early_stopping
+    }
+elif gen_mode == "sampling":
+    do_sample = True
+    top_k = st.sidebar.number_input("top_k", value=30, min_value=0)
+    top_p = st.sidebar.number_input("top_p", value=0, min_value=0)  
+    temperature = st.sidebar.number_input("temperature", value=0.7, min_value=0.0)
+    gen_params = {
+        "do_sample": do_sample,
+        "top_k": top_k,
+        "top_p": top_p,
+        "temperature": temperature
+    }
+
+def generate_caption(url):
+    image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
+    inputs = feature_extractor(image, return_tensors="pt")
+    model = get_model()
+    model.eval()
+    generated_ids = model.generate(
+        inputs["pixel_values"],
+        max_length=20,
+        no_repeat_ngram_size=2,
+        num_return_sequences=3,
+        **gen_params
+    )
+    captions = tokenizer.batch_decode(
+        generated_ids,
+        skip_special_tokens=True,
+    )
+    return captions[0]
+
+
+url = st.text_input(
+    "Insert your URL", "https://iheartcats.com/wp-content/uploads/2015/08/c84.jpg"
+)
+
+st.image(url)
+
+if st.button("Run captioning"):
+    with st.spinner("Processing image..."):
+        caption = generate_caption(url)
+        st.text(caption)