Duplicate from Celestinian/Prompt-Generator

Co-authored-by: Skyler Celestinian-Sterling <Celestinian@users.noreply.huggingface.co>

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README.md

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+---
+title: Prompt Generator
+emoji: ⚡
+colorFrom: red
+colorTo: yellow
+sdk: gradio
+sdk_version: 3.27.0
+app_file: app.py
+pinned: false
+license: mit
+duplicated_from: Celestinian/Prompt-Generator
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+from transformers import AutoTokenizer, AutoModelForCausalLM, GPT2LMHeadModel
+import gradio as gr
+import torch
+import git
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+tokenizer = AutoTokenizer.from_pretrained("Celestinian/PromptGPT")
+model = AutoModelForCausalLM.from_pretrained("Celestinian/PromptGPT")
+
+def generate_text(prompt, max_length, do_sample, temperature, no_repeat_ngram_size, top_k, top_p):
+    formatted_prompt = "\n" + prompt
+    if not ',' in prompt:
+        formatted_prompt += ','
+    prompt = tokenizer(formatted_prompt, return_tensors='pt')
+    prompt = {key: value.to(device) for key, value in prompt.items()}
+    out = model.generate(**prompt, max_length=max_length, do_sample=do_sample, temperature=temperature,
+                          no_repeat_ngram_size=no_repeat_ngram_size, top_k=top_k, top_p=top_p)
+    output = tokenizer.decode(out[0])
+    clean_output = output.replace('\n', '\n')
+    print(clean_output)
+    return clean_output
+
+input_text = gr.inputs.Textbox(lines=5, label="Input Text")
+max_length = gr.inputs.Slider(minimum=10, maximum=100, default=30, label="Max Length")
+do_sample = gr.inputs.Checkbox(default=True, label="Do Sample")
+temperature = gr.inputs.Slider(minimum=0.1, maximum=1.0, step=0.1, default=0.4, label="Temperature")
+no_repeat_ngram_size = gr.inputs.Slider(minimum=1, maximum=10, default=1, label="No Repeat N-Gram Size")
+top_k = gr.inputs.Slider(minimum=1, maximum=100, default=50, label="Top K")
+top_p = gr.inputs.Slider(minimum=0.1, maximum=1.0, step=0.1, default=0.2, label="Top P")
+
+output_text = gr.outputs.Textbox(label="Generated Text")
+
+gr.Interface(generate_text, inputs=[input_text, max_length, do_sample, temperature, no_repeat_ngram_size, top_k, top_p],
+             outputs=output_text).launch()

requirements.txt

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+transformers==4.28.1
+torch==2.0.0
+torchvision==0.15.1
+torchaudio==2.0.1
+gitpython==3.1.24
+accelerate==0.18.0