app.py

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

# Page Config

st.set_page_config(
page_title="AI Text Generator",
page_icon="🤖",
layout="wide"
)

# Sidebar

st.sidebar.title("⚙️ Settings")

model_path = st.sidebar.text_input(
"Model Path",
value="gpt2"   # change to ./results if fine-tuned
)

max_length = st.sidebar.slider("Max Length", 50, 500, 150)
temperature = st.sidebar.slider("Temperature (Creativity)", 0.5, 1.5, 0.8)
top_k = st.sidebar.slider("Top-K", 10, 100, 50)
top_p = st.sidebar.slider("Top-P", 0.5, 1.0, 0.95)

device = "cuda" if torch.cuda.is_available() else "cpu"
st.sidebar.write(f"Device: **{device.upper()}**")

# Title

st.title("🤖 Professional AI Text Generator")
st.markdown("Generate creative and grammatically correct text using a GPT-based model.")

# Load Model (cached)

@st.cache_resource
def load_model(path):
tokenizer = AutoTokenizer.from_pretrained(path)
tokenizer.pad_token = tokenizer.eos_token
model = AutoModelForCausalLM.from_pretrained(path)
model.to(device)
model.eval()
return tokenizer, model

tokenizer, model = load_model(model_path)

# Input Area

col1, col2 = st.columns([2, 1])

with col1:
prompt = st.text_area(
"Enter your prompt:",
height=200,
placeholder="Example: Alice was walking through the forest when..."
)

with col2:
st.info("Tips:\n- Higher temperature = more creative\n- Lower temperature = more accurate\n- Use your fine-tuned model for best results")

# Generate Button

if st.button("✨ Generate Text", use_container_width=True):
if prompt.strip() == "":
st.warning("Please enter a prompt.")
else:
with st.spinner("Generating..."):
inputs = tokenizer(prompt, return_tensors="pt").to(device)

```
        output = model.generate(
            **inputs,
            max_length=max_length,
            temperature=temperature,
            top_k=top_k,
            top_p=top_p,
            do_sample=True,
            pad_token_id=tokenizer.eos_token_id
        )

        generated_text = tokenizer.decode(output[0], skip_special_tokens=True)

    st.subheader("Generated Output")
    st.write(generated_text)

    # Download option
    st.download_button(
        label="📥 Download Text",
        data=generated_text,
        file_name="generated_text.txt",
        mime="text/plain"
    )
```

# Footer

st.markdown("---")
st.markdown("Built with ❤️ using Streamlit + Transformers")