Create app.py

app.py

@@ -0,0 +1,66 @@
+import streamlit as st
+import os
+from PIL import Image
+from transformers import VisionEncoderDecoderModel, ViTFeatureExtractor, AutoTokenizer
+import torch
+from nltk.corpus import wordnet
+import nltk
+
+nltk.download('wordnet')
+
+# Load the pre-trained model for image captioning
+model_name = "nlpconnect/vit-gpt2-image-captioning"
+model = VisionEncoderDecoderModel.from_pretrained(model_name)
+feature_extractor = ViTFeatureExtractor.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+def generate_caption(image):
+    pixel_values = feature_extractor(images=image, return_tensors="pt").pixel_values
+    output_ids = model.generate(pixel_values)
+    caption = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+    return caption
+
+def get_synonyms(word):
+    synonyms = set()
+    for syn in wordnet.synsets(word):
+        for lemma in syn.lemmas():
+            synonyms.add(lemma.name())
+    return synonyms
+
+def search_captions(query, captions):
+    query_words = query.split()
+    query_synonyms = set(query_words)
+    for word in query_words:
+        query_synonyms.update(get_synonyms(word))
+    
+    results = []
+    for path, caption in captions.items():
+        if any(word in caption.split() for word in query_synonyms):
+            results.append((path, caption))
+    
+    return results
+
+def main():
+    st.title("Image Gallery with Captioning and Search")
+
+    folder_path = st.text_input("Enter the folder path containing images:")
+    
+    if folder_path and os.path.isdir(folder_path):
+        image_files = [f for f in os.listdir(folder_path) if f.lower().endswith(('png', 'jpg', 'jpeg'))]
+        captions = {}
+
+        for image_file in image_files:
+            image_path = os.path.join(folder_path, image_file)
+            image = Image.open(image_path)
+            caption = generate_caption(image)
+            captions[image_path] = caption
+            st.image(image, caption=caption)
+        
+        query = st.text_input("Search images by caption:")
+        if query:
+            results = search_captions(query, captions)
+            for image_path, caption in results:
+                st.image(image_path, caption=caption)
+
+if __name__ == "__main__":
+    main()