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from transformers import pipeline
import gradio as gr
from diffusers import DiffusionPipeline
# 1. text summarizer
summarizer = pipeline("summarization", model = "facebook/bart-large-cnn")
def get_summary(text):
output = summarizer(text)
return output[0]["summary_text"]
# 2. named entity recognition
ner_model = pipeline("ner", model = "dslim/bert-large-NER")
def get_ner(text):
output = ner_model(text)
return {"text":text, "entities":output}
# 3. Image Captioning
caption_model = pipeline("image-to-text", model = "Salesforce/blip-image-captioning-base")
def get_caption(img):
output = caption_model(img)
return output[0]["generated_text"]
# 4. Image Generation
# img_model = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
img_model = DiffusionPipeline.from_pretrained("Linaqruf/anything-v3.0")
def get_img(prompt):
print("Here inside")
img = img_model(prompt)
print("Generated")
return img.images[0]
demo = gr.Blocks()
with demo:
gr.Markdown("# Try out some cool tasks!")
with gr.Tab("Text Summarization"):
sum_input = [gr.Textbox(label="Text to Summarize", placeholder="Enter text to summarize...", lines=4)]
sum_btn = gr.Button("Summarize text")
sum_output = [gr.Textbox(label="Summarized Text")]
sum_btn.click(get_summary, sum_input, sum_output)
with gr.Tab("Named Entity Recognition"):
ner_input = [gr.Textbox(label="Text to find Entities", placeholder = "Enter text...", lines = 4)]
ner_btn = gr.Button("Generate entities")
ner_output = [gr.HighlightedText(label="Text with entities")]
ner_btn.click(get_ner, ner_input, ner_output)
with gr.Tab("Image Captioning"):
cap_input = [gr.Image(label="Upload Image", type="pil")]
cap_btn = gr.Button("Generate Caption")
cap_output = [gr.Textbox(label="Caption")]
cap_btn.click(get_caption, cap_input, cap_output)
with gr.Tab("Image Generation"):
img_input = [gr.Textbox(label="Your Text")]
img_btn = gr.Button("Generate Image")
img_output = [gr.Image(label="Generated Image")]
img_btn.click(get_img, img_input, img_output)
demo.launch() |