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e6a70da 40177aa e6a70da 4feed37 e6a70da e6905ea e6a70da 4feed37 e6a70da e6905ea e6a70da | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 | import gradio as gr
from transformers import pipeline
# Models
models = {
"text_generator": {"model": "EleutherAI/gpt-neo-1.3B", "task": "text-generation"},
"sentiment": {"model": "cardiffnlp/twitter-roberta-base-sentiment-latest", "task": "sentiment-analysis"},
"dialogue": {"model": "microsoft/DialoGPT-medium", "task": "text-generation"},
"summarizer": {"model": "sshleifer/distilbart-cnn-12-6", "task": "summarization"},
"ner": {"model": "dslim/bert-base-NER", "task": "ner"},
}
# Initialize pipelines
pipelines = {}
for key, info in models.items():
pipelines[key] = pipeline(info["task"], model=info["model"])
def generate_text(prompt, max_length):
result = pipelines["text_generator"](prompt, max_length=int(max_length), num_return_sequences=1)
return result[0]['generated_text']
def analyze_sentiment(text):
result = pipelines["sentiment"](text)
return [{"label": res['label'], "score": f"{res['score']:.2f}"} for res in result]
def converse(message, state):
chat_history_str = state or ""
chat_input_ids = pipelines["dialogue"].tokenizer.encode(chat_history_str + pipelines["dialogue"].tokenizer.eos_token + message, return_tensors='pt')
bot_input_ids = pipelines["dialogue"].model.generate(chat_input_ids, max_length=1000, pad_token_id=pipelines["dialogue"].tokenizer.eos_token_id)
bot_response = pipelines["dialogue"].tokenizer.decode(bot_input_ids[:, chat_input_ids.shape[-1]:][0], skip_special_tokens=True)
chat_history = chat_history_str + pipelines["dialogue"].tokenizer.eos_token + message + pipelines["dialogue"].tokenizer.eos_token + bot_response
return bot_response, chat_history
def summarize_text(text, max_length, min_length):
result = pipelines["summarizer"](text, max_length=int(max_length), min_length=int(min_length), do_sample=False)
return result[0]['summary_text']
def ner_analysis(text):
result = pipelines["ner"](text, aggregation_strategy="max")
formatted = [f"{res['entity_group']}: {res['word']} ({res['score']:.2f})" for res in result]
return "\n".join(formatted)
with gr.Blocks() as demo:
gr.Markdown("# AI Art Main Interface")
gr.Markdown("Use various AI models below")
with gr.Tab("Text Generator"):
gr.Markdown("## Text Generator (EleutherAI/gpt-neo-1.3B)")
gen_prompt = gr.Textbox(label="Prompt")
gen_len = gr.Slider(10, 200, 50, label="Max Length")
gen_btn = gr.Button()
gen_out = gr.Textbox(label="Output")
gen_btn.click(generate_text, [gen_prompt, gen_len], gen_out)
with gr.Tab("Sentiment Analysis"):
gr.Markdown("## Sentiment Analyzer (cardiffnlp/twitter-roberta-base-sentiment-latest)")
sent_text = gr.Textbox(label="Text")
sent_btn = gr.Button()
sent_out = gr.JSON(label="Result")
sent_btn.click(analyze_sentiment, sent_text, sent_out)
with gr.Tab("Dialogue Bot"):
gr.Markdown("## Dialogue Bot (microsoft/DialoGPT-medium)")
chat_state = gr.State()
chat_msg = gr.Textbox(label="Message")
chat_send = gr.Button()
chat_out = gr.Chatbot()
chat_send.click(converse, [chat_msg, chat_state], [chat_out, chat_state])
with gr.Tab("Text Summarizer"):
gr.Markdown("## Text Summarizer (sshleifer/distilbart-cnn-12-6)")
sum_text = gr.Textbox(lines=5, label="Text")
sum_max = gr.Slider(10, 200, 100, label="Max Length")
sum_min = gr.Slider(5, 50, 10, label="Min Length")
sum_btn = gr.Button()
sum_out = gr.Textbox(label="Summary")
sum_btn.click(summarize_text, [sum_text, sum_max, sum_min], sum_out)
with gr.Tab("Named Entity Recognition"):
gr.Markdown("## Named Entity Recognition (dslim/bert-base-NER)")
ner_text = gr.Textbox(lines=3, label="Text")
ner_btn = gr.Button()
ner_out = gr.Textbox(lines=5, label="Entities")
ner_btn.click(ner_analysis, ner_text, ner_out)
if __name__ == "__main__":
demo.launch()
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