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()

this dictionary have limited terms, give me dictionary with thousands of term related physics. also define term in urdu and english with daily life example