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| # Gradio Interface | |
| import gradio as gr | |
| import numpy as np | |
| import pandas as pd | |
| from sklearn.metrics.pairwise import cosine_similarity | |
| from sentence_transformers import SentenceTransformer | |
| import requests | |
| from PIL import Image | |
| from transformers import BlipProcessor, BlipForConditionalGeneration | |
| sentence_model = SentenceTransformer("all-MiniLM-L6-v2") | |
| processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base") | |
| image_model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base") | |
| def generate_input(input_type, image=None, text=None, response_amount=3): | |
| # initalize input variable | |
| combined_input = "" | |
| # handle image input if chosen | |
| if input_type == "Image" and image: | |
| inputs = processor(images=image, return_tensors="pt") #process image with BlipProcessor | |
| out = image_model.generate(**inputs) #generate caption with BlipModel | |
| image_caption = processor.decode(out[0], skip_special_tokens=True) #decode output w/ processor | |
| combined_input += image_caption # add the image caption to input | |
| # handle text input if chosen | |
| elif input_type == "Text" and text: | |
| combined_input += text # add the text to input | |
| # handle both text and image input if chosen | |
| elif input_type == "Both" and image and text: | |
| inputs = processor(images=image, return_tensors="pt") | |
| out = image_model.generate(**inputs) | |
| image_caption = processor.decode(out[0], skip_special_tokens=True) #repeat image processing + caption generation and decoding | |
| combined_input += image_caption + " and " + text # combine image caption and text | |
| # if no input, fallback | |
| if not combined_input: | |
| combined_input = "No input provided." | |
| if response_amount is None: | |
| response_amount=3 | |
| return vector_search(combined_input,response_amount) #search through embedded document w/ input | |
| # load embeddings and metadata | |
| embeddings = np.load("netflix_embeddings.npy") #created using sentence_transformers on kaggle | |
| metadata = pd.read_csv("netflix_metadata.csv") #created using sentence_transformers on kaggle | |
| # vector search function | |
| def vector_search(query,top_n=3): | |
| query_embedding = sentence_model.encode(query) #encode input w/ Sentence Transformers | |
| similarities = cosine_similarity([query_embedding], embeddings)[0] #similarity function | |
| if top_n is None: | |
| top_n=3 | |
| top_indices = similarities.argsort()[-top_n:][::-1] #return top n indices based on chosen output amount | |
| results = metadata.iloc[top_indices] #get metadata | |
| result_text="" | |
| for index,row in results.iterrows(): #loop through results to get Title, Description, and Genre for top n outputs | |
| if index!=top_n-1: | |
| result_text+=f"Title: {row['title']} Description: {row['description']} Genre: {row['listed_in']}\n\n" | |
| else: | |
| result_text+=f"Title: {row['title']} Description: {row['description']} Genre: {row['listed_in']}" | |
| return result_text | |
| def set_response_amount(response_amount): #set response amount | |
| if response_amount is None: | |
| return 3 | |
| return response_amount | |
| # based on the selected input type, make the appropriate input visible | |
| def update_inputs(input_type): | |
| if input_type == "Image": | |
| return gr.update(visible=True), gr.update(visible=False), gr.update(visible=True) | |
| elif input_type == "Text": | |
| return gr.update(visible=False), gr.update(visible=True), gr.update(visible=True) | |
| elif input_type == "Both": | |
| return gr.update(visible=True), gr.update(visible=True), gr.update(visible=True) | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# Netflix Recommendation System") | |
| gr.Markdown("Enter a query to receive Netflix show recommendations based on title, description, and genre.") | |
| input_type = gr.Radio(["Image", "Text", "Both"], label="Select Input Type", type="value") | |
| response_type=gr.Dropdown(choices=[3,5,10,25], type="value", label="Select Response Amount", visible=False) | |
| image_input = gr.Image(label="Upload Image", type="pil", visible=False) # Hidden initially | |
| text_input = gr.Textbox(label="Enter Text Query", placeholder="Enter a description or query here", visible=False) # hidden initially | |
| input_type.change(fn=update_inputs, inputs=input_type, outputs=[image_input, text_input, response_type]) | |
| # state variable to store the selected response amount | |
| selected_response_amount = gr.State() | |
| # capture response amount immediately when dropdown changes | |
| response_type.change(fn=set_response_amount, inputs=response_type, outputs=selected_response_amount) | |
| submit_button = gr.Button("Submit") | |
| output = gr.Textbox(label="Recommendations") | |
| if selected_response_amount is None: | |
| selected_response_amount=3 | |
| submit_button.click(fn=generate_input, inputs=[input_type,image_input, text_input,selected_response_amount], outputs=output) | |
| demo.launch() | |