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app.py
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import gradio as gr
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import requests
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# GPT-J-6B API
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API_URL = "https://api-inference.huggingface.co/models/EleutherAI/gpt-j-6B"
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headers = {"Authorization": "Bearer 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"}
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prompt = """
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word: risk
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poem using word: And then the day came,
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when the risk
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to remain tight
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in a bud
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was more painful
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than the risk
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it took
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to blossom.
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word: bird
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poem using word: She sights a bird, she chuckles
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She flattens, then she crawls
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She runs without the look of feet
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Her eyes increase to Balls.
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word: """
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examples = [["river"], ["night"], ["trees"],["table"],["laughs"]]
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def poem_generate(word):
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p = prompt + word.lower() + "\n" + "poem using word: "
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print(f"*****Inside poem_generate - Prompt is :{p}")
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json_ = {"inputs": p,
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"parameters":
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{
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"top_p": 0.9,
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"temperature": 1.1,
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"max_new_tokens": 50,
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"return_full_text": False
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}}
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response = requests.post(API_URL, headers=headers, json=json_)
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output = response.json()
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print(f"Was there an error? Reason is : {output}")
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output_tmp = output[0]['generated_text']
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print(f"GPTJ response without splits is: {output_tmp}")
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#poem = output[0]['generated_text'].split("\n\n")[0] # +"."
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if "\n\n" not in output_tmp:
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if output_tmp.find('.') != -1:
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idx = output_tmp.find('.')
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poem = output_tmp[:idx+1]
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else:
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idx = output_tmp.rfind('\n')
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poem = output_tmp[:idx]
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else:
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poem = output_tmp.split("\n\n")[0] # +"."
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print(f"Poem being returned is: {poem}")
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return poem
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def poem_to_image(poem):
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print("*****Inside Poem_to_image")
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poem = " ".join(poem.split('\n'))
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poem = poem + " oil on canvas."
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steps, width, height, images, diversity = '50','256','256','1',15
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img = gr.Interface.load("spaces/multimodalart/latentdiffusion")(poem, steps, width, height, images, diversity)[0]
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return img
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demo = gr.Blocks()
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with demo:
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gr.Markdown("<h1><center>Generate Short Poem along with an Illustration</center></h1>")
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gr.Markdown(
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"<div>Enter a single word you would want GPTJ-6B to write Poetry π€ on.</div>"
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"<div>Generate an illustration π¨ provided by Latent Diffusion model.</div><div>GPJ-6B is a 6 Billion parameter autoregressive language model. It generates the Poem based on how it has been 'prompt-engineered' π€ The complete text of generated poem then goes in as a prompt to the amazing Latent Diffusion Art space by <a href='https://huggingface.co/spaces/multimodalart/latentdiffusion' target='_blank'>Multimodalart</a>.</div>Please note that some of the Poems/Illustrations might not look at par, and well, this is what happens when you can't 'cherry-pick' and post π <div> Some of the example words that you can use are 'river', 'night', 'trees', 'table', 'laughs' or maybe on similar lines to get best results!"
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)
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with gr.Row():
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input_word = gr.Textbox(placeholder="Enter a word here to create a Poem on..")
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poem_txt = gr.Textbox(lines=7)
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output_image = gr.Image(type="filepath", shape=(256,256))
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b1 = gr.Button("Generate Poem")
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b2 = gr.Button("Generate Image")
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b1.click(poem_generate, input_word, poem_txt)
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b2.click(poem_to_image, poem_txt, output_image)
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#examples=examples
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demo.launch(enable_queue=True, debug=True)
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