app.py

import transformers
import streamlit as st

# Load the English-to-Urdu translation model
model_name = "Helsinki-NLP/opus-mt-en-ur"
tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
model = transformers.AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Create a dictionary of physics terms
physics_terms = {
    "force": "قوت",
    "mass": "جرم",
    "acceleration": "تسارع",
    # Add more terms here
}

# Streamlit app
def main():
    st.title("Physics Terms Dictionary")

    term = st.text_input("Enter a physics term:")

    if term:
        if term in physics_terms:
            urdu_term = physics_terms[term]
            st.write(f"Urdu Term: {urdu_term}")

            # Translate the explanation using the model
            inputs = tokenizer(term, return_tensors="pt")
            outputs = model.generate(**inputs)
            translated_explanation = tokenizer.decode(outputs[0], skip_special_tokens=True)
            st.write(f"Explanation: {translated_explanation}")
        else:
            st.warning("Term not found in the dictionary.")

if __name__ == "__main__":
    main()