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| import streamlit as st | |
| from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline | |
| def main(): | |
| st.set_page_config(page_title="LinguaSphere: Aya by CohereAI", layout="wide") | |
| st.title("LinguaSphere: Aya by CohereAI") | |
| # Load translation model and tokenizer | |
| translation_checkpoint = "CohereForAI/aya-101" | |
| translation_tokenizer = AutoTokenizer.from_pretrained(translation_checkpoint) | |
| translation_model = AutoModelForSeq2SeqLM.from_pretrained(translation_checkpoint) | |
| # Load sentiment analysis model | |
| sentiment_analysis_pipeline = pipeline("sentiment-analysis", model="CohereForAI/aya-101") | |
| # Load named entity recognition (NER) model | |
| ner_pipeline = pipeline("ner", model="CohereForAI/aya-101") | |
| # Load summarization model | |
| summarization_pipeline = pipeline("summarization", model="CohereForAI/aya-101") | |
| # Sidebar options | |
| st.sidebar.title("Options") | |
| show_model_info = st.sidebar.checkbox("Show Model Information") | |
| show_task_description = st.sidebar.checkbox("Show Task Description") | |
| show_about = st.sidebar.checkbox("About") | |
| if show_about: | |
| st.sidebar.subheader("About") | |
| st.sidebar.markdown( | |
| "LinguaSphere is a Streamlit app powered by Aya, a multilingual model developed by CohereAI." | |
| ) | |
| if show_model_info: | |
| st.sidebar.subheader("Model Information") | |
| st.sidebar.markdown( | |
| """ | |
| The Aya model is a massively multilingual generative language model developed by Cohere For AI. | |
| It is capable of performing various natural language processing tasks such as translation, sentiment analysis, | |
| named entity recognition, and summarization. | |
| """ | |
| ) | |
| if show_task_description: | |
| st.sidebar.subheader("Task Descriptions") | |
| st.sidebar.markdown( | |
| """ | |
| - **Translation:** Translate text from one language to another. | |
| - **Text Generation:** Generate text based on a prompt. | |
| - **Sentiment Analysis:** Analyze the sentiment of text. | |
| - **Named Entity Recognition:** Identify named entities in text. | |
| - **Summarization:** Generate a summary of text. | |
| """ | |
| ) | |
| task = st.selectbox( | |
| "Select Task", | |
| [ | |
| "Translation", | |
| "Text Generation", | |
| "Sentiment Analysis", | |
| "Named Entity Recognition", | |
| "Summarization", | |
| ], | |
| ) | |
| if task == "Translation": | |
| source_language = st.selectbox( | |
| "Source Language", ["English", "Turkish", "Hindi"] | |
| ) | |
| target_language = st.selectbox( | |
| "Target Language", ["Nepali", "Turkish", "English"] | |
| ) | |
| source_text = st.text_area("Enter text in " + source_language, "") | |
| if st.button("Translate"): | |
| source_text = source_text.strip() | |
| if source_language == "English": | |
| source_text = "Translate to " + target_language + ": " + source_text | |
| else: | |
| source_text = source_text + "।" | |
| inputs = translation_tokenizer.encode(source_text, return_tensors="pt") | |
| outputs = translation_model.generate(inputs, max_length=128) | |
| translated_text = translation_tokenizer.decode( | |
| outputs[0], skip_special_tokens=True | |
| ) | |
| st.write("Translated text in " + target_language + ":", translated_text) | |
| elif task == "Text Generation": | |
| prompt = st.text_area("Enter prompt for text generation", "") | |
| language = st.selectbox("Select Language", ["English", "Turkish", "Hindi"]) | |
| if st.button("Generate"): | |
| prompt = prompt.strip() | |
| if language == "English": | |
| prompt = "Generate text: " + prompt | |
| elif language == "Hindi": | |
| prompt = prompt + "।" | |
| inputs = translation_tokenizer.encode(prompt, return_tensors="pt") | |
| outputs = translation_model.generate(inputs, max_length=128) | |
| generated_text = translation_tokenizer.decode( | |
| outputs[0], skip_special_tokens=True | |
| ) | |
| st.write("Generated text:", generated_text) | |
| elif task == "Sentiment Analysis": | |
| text = st.text_area("Enter text for sentiment analysis", "") | |
| if st.button("Analyze Sentiment"): | |
| result = sentiment_analysis_pipeline(text) | |
| st.write("Sentiment:", result[0]["label"]) | |
| elif task == "Named Entity Recognition": | |
| text = st.text_area("Enter text for named entity recognition", "") | |
| if st.button("Recognize Entities"): | |
| entities = ner_pipeline(text) | |
| st.write("Entities:") | |
| for entity in entities: | |
| st.write(entity) | |
| elif task == "Summarization": | |
| text = st.text_area("Enter text for summarization", "") | |
| if st.button("Summarize"): | |
| summary = summarization_pipeline(text) | |
| st.write("Summary:", summary[0]["summary_text"]) | |
| if __name__ == "__main__": | |
| main() | |