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import streamlit as st
from unsloth import FastLanguageModel
import torch
# Load the model and tokenizer
max_seq_length = 2048
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.
# Initialize the model
model, tokenizer = FastLanguageModel.from_pretrained(
model_name="suhaifLLM/unsloth-llama3-8b-instruct-4bit",
max_seq_length=max_seq_length,
dtype=dtype,
load_in_4bit=load_in_4bit
)
# Default instruction
default_instruction = "You are a creative writer. Based on the given input, generate a well-structured story with an engaging plot, well-developed characters, and immersive details. Ensure the story has a clear beginning, middle, and end. Include dialogue and descriptions to bring the story to life. You can add a twist to the story also."
def format_prompt(input_text, instruction=default_instruction):
return f"{instruction}\n\nInput:\n{input_text}\n\nResponse:\n"
# Streamlit App
st.title("Interactive Storytelling Assistant")
st.write("Create your story prompt and receive story suggestions!")
# User input for story prompt
user_input = st.text_area("Enter your story idea:", "A young adventurer embarks on a journey to find a lost treasure.")
generate_story = st.button("Generate Story")
if generate_story and user_input:
# Prepare inputs for the model
inputs = tokenizer(
[format_prompt(user_input)],
return_tensors="pt"
).to("cuda")
# Generate story
outputs = model.generate(**inputs, max_new_tokens=500, use_cache=True)
generated_story = tokenizer.decode(outputs[0], skip_special_tokens=True)
# Display generated story
st.subheader("Generated Story:")
st.write(generated_story)
# Feedback mechanism
st.subheader("Rate the Story")
story_rating = st.slider("How would you rate this story?", 1, 5)
user_feedback = st.text_area("Additional Feedback/Suggestions:")
if st.button("Submit Feedback"):
st.write("Thank you for your feedback!")
# Process feedback (In a real scenario, this would be saved to a database)
# Community engagement
st.subheader("Share Your Story")
user_story = st.text_area("Write or paste your own story here:")
if st.button("Share Story"):
st.write("Thank you for sharing your story!")
# Save the story (In a real scenario, this would be saved to a shared community platform)
# Display shared stories (Placeholder example)
st.subheader("Community Stories")
st.write("Story 1: An epic tale of adventure...") # Placeholder for actual stories
# Critique section
st.text_area("Leave a critique for Story 1:")
if st.button("Submit Critique"):
st.write("Thank you for your critique!") |