app.py

import streamlit as st
from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer


## Function To get response from LLAma 2 model

def getLLamaresponse(input_text):
    ### LLama2 model
    # Load the fine-tuned model and tokenizer
    model_name = "Jithendra-k/InterACT_mini"
    model = AutoModelForCausalLM.from_pretrained(model_name)
    tokenizer = AutoTokenizer.from_pretrained(model_name)

    # Define the input prompt
    #prompt = "I want to drink water"

    # Run text generation pipeline with the model
    pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=50, do_sample=True, repetition_penalty=1.9)
    result = pipe(f"<s>[INST] {input_text} [/INST]")
    # print(result[0]['generated_text'])
    return result[0]['generated_text']

st.set_page_config(page_title="Generate Keywords from User Queries",
                   page_icon='🤖',
                   layout='centered',
                   initial_sidebar_state='collapsed')

st.header("Generate keywords from User queries 🤖")

input_text =st.text_input("Enter the query")

submit =st.button("Generate")

## Final response
if submit:
    st.write(getLLamaresponse(input_text))