app.py

# We'll generate a sample Streamlit app for LLM fine-tuning and deployment simulation.
# Since we can't actually fine-tune large models in this script due to constraints, 
# we'll simulate the UI and interaction as if the model was already fine-tuned.

import os

# Create a simple streamlit app template
streamlit_app_code = """
import streamlit as st
from transformers import pipeline

st.set_page_config(page_title="LLM Fine-Tuned Chatbot", page_icon="🧠", layout="wide")

# Custom CSS styling
st.markdown(\"""
    <style>
        .main {
            background-color: #f4f4f9;
        }
        .stTextInput>div>div>input {
            border-radius: 10px;
        }
        .stButton>button {
            background-color: #4CAF50;
            color: white;
            border-radius: 10px;
            height: 3em;
            width: 100%;
        }
    </style>
\""", unsafe_allow_html=True)

st.title("🧠 Fine-Tuned LLM Chatbot")
st.subheader("Chat with your own fine-tuned LLM model")

# Sidebar Info
st.sidebar.title("Model Info")
st.sidebar.info("This chatbot uses a fine-tuned LLM (simulated via Hugging Face pipeline)")

# Load pipeline (simulation for actual fine-tuned model)
@st.cache_resource
def load_pipeline():
    # Replace with your fine-tuned model, e.g., "your-username/your-fine-tuned-model"
    return pipeline("text-generation", model="gpt2")

generator = load_pipeline()

# User input
user_input = st.text_input("Enter your prompt here")

if st.button("Generate Response"):
    if user_input.strip() != "":
        with st.spinner("Generating response..."):
            response = generator(user_input, max_length=100, do_sample=True)[0]['generated_text']
            st.success("Response:")
            st.write(response)
    else:
        st.warning("Please enter a prompt to generate response.")
"""

# Save to file for deployment
file_path = "/mnt/data/llm_finetuned_chatbot.py"
with open(file_path, "w") as f:
    f.write(streamlit_app_code)

file_path