main.py

import streamlit as st
import json
import torch
from transformers import AutoTokenizer
from modelling_cnn import CNNForNER, SentimentCNNModel


# Load the Yoruba NER model
ner_model_name = "./my_model/pytorch_model.bin"
model_ner = "Testys/cnn_yor_ner"
ner_tokenizer = AutoTokenizer.from_pretrained(model_ner)
with open("./my_model/config.json", "r") as f:
    ner_config = json.load(f)

ner_model = CNNForNER(
                      pretrained_model_name=ner_config["pretrained_model_name"],
                      num_classes=ner_config["num_classes"]
                      )
ner_model.load_state_dict(torch.load(ner_model_name, map_location=torch.device('cpu')))
ner_model.eval()

# Load the Yoruba sentiment analysis model
sentiment_model_name = "./sent_model/sent_pytorch_model.bin"
model_sent = "Testys/cnn_sent_yor"
sentiment_tokenizer = AutoTokenizer.from_pretrained(model_sent)

with open("./sent_model/config.json", "r") as f:
    sentiment_config = json.load(f)

sentiment_model = SentimentCNNModel(
                                    transformer_model_name=sentiment_config["pretrained_model_name"],
                                    num_classes=sentiment_config["num_classes"]
                                    )

sentiment_model.load_state_dict(torch.load(sentiment_model_name, map_location=torch.device('cpu')))
sentiment_model.eval()


def analyze_text(text):
    # Tokenize input text for NER
    ner_inputs = ner_tokenizer(text, return_tensors="pt")

    # Perform Named Entity Recognition
    with torch.no_grad():
        ner_outputs = ner_model(**ner_inputs)
    
    ner_predictions = torch.argmax(ner_outputs.logits, dim=-1)
    ner_labels = [ner_tokenizer.decode(token) for token in ner_predictions[0]]

    # Tokenize input text for sentiment analysis
    sentiment_inputs = sentiment_tokenizer.encode_plus(text, return_tensors="pt")

    # Perform sentiment analysis
    with torch.no_grad():
        sentiment_outputs = sentiment_model(**sentiment_inputs)
    sentiment_probabilities = torch.softmax(sentiment_outputs.logits, dim=1)
    sentiment_scores = sentiment_probabilities.tolist()

    return ner_labels, sentiment_scores

def main():
    st.title("YorubaCNN Models for NER and Sentiment Analysis")

    # Input text
    text = st.text_area("Enter Yoruba text", "")

    if st.button("Analyze"):
        if text:
            ner_labels, sentiment_scores = analyze_text(text)

            # Display Named Entities
            st.subheader("Named Entities")
            for label in ner_labels:
                st.write(f"- {label}")

            # Display Sentiment Analysis
            st.subheader("Sentiment Analysis")
            st.write(f"Positive: {sentiment_scores[2]:.2f}")
            st.write(f"Negative: {sentiment_scores[0]:.2f}")
            st.write(f"Neutral: {sentiment_scores[1]:.2f}")

if __name__ == "__main__":
    main()