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| import openai | |
| import os | |
| import streamlit as st | |
| # Access repository secret | |
| api_key = os.environ.get('OPENAI_API_KEY') | |
| # Check if the secret is available | |
| if api_key: | |
| st.write("API Key:", api_key) | |
| else: | |
| st.write("API Key not found.") | |
| import numpy as np | |
| from PIL import Image | |
| from transformers import AutoTokenizer, ViTFeatureExtractor, VisionEncoderDecoderModel, GPT2Tokenizer, GPT2LMHeadModel | |
| # Directory path to the saved model on Google Drive | |
| model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning") | |
| feature_extractor = ViTFeatureExtractor.from_pretrained("nlpconnect/vit-gpt2-image-captioning") | |
| tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning") | |
| def generate_captions(image): | |
| image = Image.open(image).convert("RGB") | |
| generated_caption = tokenizer.decode(model.generate(feature_extractor(image, return_tensors="pt").pixel_values.to("cpu"))[0]) | |
| sentence = generated_caption | |
| text_to_remove = "<|endoftext|>" | |
| generated_caption = sentence.replace(text_to_remove, "") | |
| return generated_caption | |
| def generate_paragraph(caption): | |
| prompt = "Generate a paragraph based on the following caption: " + caption | |
| # Make the API call to GPT-3 | |
| response = openai.Completion.create( | |
| engine='text-davinci-003', # Specify the GPT-3 model | |
| prompt=prompt, | |
| max_tokens=200, # Adjust the desired length of the generated text | |
| n = 1, # Set the number of completions to generate | |
| stop=None, # Specify an optional stop sequence | |
| temperature=0.7 # Adjust the temperature for randomness (between 0 and 1) | |
| ) | |
| # Extract the generated text from the API response | |
| generated_text = response.choices[0].text.strip() | |
| return generated_text | |
| # create the Streamlit app | |
| def app(): | |
| st.title('Image from your Side, Detailed description from my site') | |
| st.write('Upload an image to see what we have in store.') | |
| # create file uploader | |
| uploaded_file = st.file_uploader("Got You Covered, Upload your wish!, magic on the Way! ", type=["jpg", "jpeg", "png"]) | |
| # check if file has been uploaded | |
| if uploaded_file is not None: | |
| # load the image | |
| image = Image.open(uploaded_file).convert("RGB") | |
| # Image Captions | |
| string = generate_captions(uploaded_file) | |
| st.image(image, caption='The Uploaded File') | |
| st.write("First is first captions for your Photo : ", string) | |
| generated_paragraph = generate_paragraph(string) | |
| st.write(generated_paragraph) | |
| # run the app | |
| if __name__ == '__main__': | |
| app() |