app.py

import streamlit as st
from langchain_ollama.llms import Ollama  # Updated import path
from langchain import LLMChain
from langchain.prompts import PromptTemplate

# Title for the Streamlit app
st.title("LLaMA 3.1 8B Instruct Model with Streamlit (Using LangChain & Ollama)")

# Load the Ollama model using LangChain
@st.cache_resource
def load_ollama_model():
    return Ollama(model="llama3.1")  # You can use other versions if needed

llama_model = load_ollama_model()

# Create a LangChain LLMChain object
prompt_template = PromptTemplate(
    input_variables=["prompt"],
    template="{prompt}"
)

llm_chain = LLMChain(
    llm=llama_model,
    prompt=prompt_template
)

# Input text from the user
user_input = st.text_area("Enter your prompt:", "")

# Generate response using the model
if st.button("Generate"):
    if user_input:
        # Generate response from LLMChain using LangChain and Ollama
        response = llm_chain.run({"prompt": user_input})
        st.text_area("Model Response:", response, height=200)
    else:
        st.warning("Please enter a prompt.")