add aya model

Signed-off-by: Ilham Syahid S <ilhamsyahids@gmail.com>

README.md

@@ -1,12 +1,12 @@
 ---
 title: CohereAya
-emoji: 👀
-colorFrom: green
-colorTo: green
+emoji: 💬
+colorFrom: blue
+colorTo: yellow
 sdk: streamlit
 sdk_version: 1.31.1
 app_file: app.py
 pinned: false
----
+license: openrail
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---

app.py

@@ -0,0 +1,128 @@
+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()

requirements.txt

@@ -0,0 +1,3 @@
+streamlit
+transformers
+torch