Update app.py
Browse files
app.py
CHANGED
@@ -1,30 +1,12 @@
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import gradio as gr
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import os
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import requests
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import random
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import time
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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from PIL import Image
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from transformers import pipeline
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from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
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# Load the pipeline for text generation
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text_generator = pipeline(
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"text-generation",
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model="Ar4ikov/gpt2-650k-stable-diffusion-prompt-generator",
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tokenizer="gpt2"
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)
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# Load tokenizer and model
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tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-2.7B")
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model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-neo-2.7B")
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# Function to generate text based on input prompt
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def generate_text(prompt):
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return text_generator(prompt, max_length=77)[0]["generated_text"]
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# Function to generate image based on input text
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def generate_image(text):
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# Tokenize input text
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input_ids = tokenizer.encode(text, return_tensors="pt")
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# Create Gradio interface
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iface = gr.Interface(
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fn=
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inputs=
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outputs=
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title="
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description="
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theme="huggingface"
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)
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# Launch
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iface.launch()
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import gradio as gr
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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from PIL import Image
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# Load tokenizer and model
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tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-2.7B")
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model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-neo-2.7B")
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def generate_image(text):
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# Tokenize input text
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input_ids = tokenizer.encode(text, return_tensors="pt")
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# Create Gradio interface
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iface = gr.Interface(
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fn=generate_image,
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inputs=gr.inputs.Textbox(lines=3, label="Input Text"),
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outputs="image",
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title="Text-to-Image Generator",
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description="Generate images from text using Hugging Face's GPT-Neo model.",
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theme="huggingface"
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)
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# Launch Gradio interface
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iface.launch()
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