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import gradio as gr
from transformers import MarianMTModel, MarianTokenizer, GPT2LMHeadModel, GPT2Tokenizer, AutoTokenizer, AutoModelForSequenceClassification
import torch
# Translation
def translate(text, target_language):
language_codes = {
"Spanish": "es",
"French (European)": "fr",
"French (Canadian)": "fr",
"Italian": "it",
"Ukrainian": "uk",
"Portuguese (Brazilian)": "pt_BR",
"Portuguese (European)": "pt",
"Russian": "ru",
"Chinese": "zh",
"Dutch": "nl",
"German": "de",
"Arabic": "ar",
"Hebrew": "he",
"Greek": "el"
}
# Text Generation
def generate_text(prompt):
text_gen = pipeline("text-generation", model="gpt2")
generated_text = text_gen(prompt, max_length=max_length, do_sample=True)[0]["generated_text"]
return generated_text
# Text Classification
def classify_text(text):
classifier = pipeline("zero-shot-classification")
result = classifier(text, labels.split(','))
scores = result["scores"]
predictions = result["labels"]
sorted_predictions = [pred for _, pred in sorted(zip(scores, predictions), reverse=True)]
return sorted_predictions
# Sentiment Analysis
def sentiment_analysis(text):
model_name = "distilbert-base-uncased-finetuned-sst-2-english"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)
sentiment_scores = torch.softmax(outputs.logits, dim=1)
sentiment = "positive" if sentiment_scores[0, 1] > sentiment_scores[0, 0] else "negative"
return sentiment
language_options = [
"Spanish", "French (European)", "French (Canadian)", "Italian", "Ukrainian",
"Portuguese (Brazilian)", "Portuguese (European)", "Russian", "Chinese",
"Dutch", "German", "Arabic", "Hebrew", "Greek"
]
iface = gr.Interface(
[translate, generate_text, classify_text, sentiment_analysis],
inputs=[
gr.inputs.Textbox(lines=5, label="Enter text to translate:"),
gr.inputs.Dropdown(choices=language_options, label="Target Language"),
gr.inputs.Textbox(lines=5, label="Enter text for text generation:"),
gr.inputs.Textbox(lines=5, label="Enter text for text classification:"),
gr.inputs.Textbox(lines=5, label="Enter text for sentiment analysis:"),
],
outputs=[
gr.outputs.Textbox(label="Translated Text"),
gr.outputs.Textbox(label="Generated Text"),
gr.outputs.Textbox(label="Classification Result"),
gr.outputs.Textbox(label="Sentiment Result"),
],
)
iface.launch()
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