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| from transformers import pipeline | |
| import streamlit as st | |
| from PIL import Image | |
| import requests | |
| from io import BytesIO | |
| from sentence_transformers import SentenceTransformer | |
| import numpy as np | |
| import faiss | |
| import pandas as pd | |
| # Initialize the image-to-text pipeline | |
| image_to_text = pipeline("image-to-text", model="nlpconnect/vit-gpt2-image-captioning") | |
| # Initialize the sentence transformer model for embeddings | |
| sentence_model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2') | |
| # Function to load images from URL | |
| def load_image_from_url(url): | |
| try: | |
| response = requests.get(url) | |
| img = Image.open(BytesIO(response.content)) | |
| return img | |
| except Exception as e: | |
| st.error(f"Error loading image from URL: {e}") | |
| return None | |
| # Load the dataset and create FAISS index | |
| def load_dataset_and_create_index(): | |
| df = pd.read_csv('/path/to/your/amazon_reviews.csv') | |
| review_texts = df['reviewText'].dropna().tolist() | |
| review_embeddings = sentence_model.encode(review_texts) | |
| dimension = review_embeddings.shape[1] | |
| faiss_index = faiss.IndexFlatL2(dimension) | |
| faiss_index.add(np.array(review_embeddings)) | |
| return faiss_index, review_texts | |
| faiss_index, review_texts = load_dataset_and_create_index() | |
| # Find top N similar reviews | |
| def find_top_n_similar_reviews(query, faiss_index, review_texts, top_n=3): | |
| query_embedding = sentence_model.encode([query]) | |
| _, indices = faiss_index.search(query_embedding, top_n) | |
| return [review_texts[i] for i in indices[0]] | |
| st.title('Image Captioning and Review Visualization Application') | |
| input_type = st.radio("Select input type:", ("Upload Image", "Image URL", "Text")) | |
| image = None | |
| text_input = "" | |
| if input_type == "Upload Image": | |
| uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"]) | |
| if uploaded_file is not None: | |
| image = Image.open(uploaded_file) | |
| st.image(image, caption='Uploaded Image', use_column_width=True) | |
| elif input_type == "Image URL": | |
| image_url = st.text_input("Enter the image URL here:", "") | |
| if image_url: | |
| image = load_image_from_url(image_url) | |
| elif input_type == "Text": | |
| text_input = st.text_area("Enter text here:", "") | |
| if st.button('Generate Caption'): | |
| result_text = "" | |
| if input_type in ["Upload Image", "Image URL"] and image: | |
| with st.spinner("Generating caption..."): | |
| result = image_to_text(image_url if input_type == "Image URL" else uploaded_file) | |
| result_text = result[0]['generated_text'] if result else "Failed to generate caption." | |
| elif input_type == "Text" and text_input: | |
| result_text = text_input | |
| if result_text: | |
| st.success(f'Generated Caption: {result_text}') | |
| similar_reviews = find_top_n_similar_reviews(result_text, faiss_index, review_texts) | |
| st.write("Similar Reviews Based on the Caption:") | |
| for review in similar_reviews: | |
| st.write(review) | |
| else: | |
| st.error("Please provide input.") | |