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import streamlit as st | |
from transformers import pipeline | |
# Load the Hugging Face model for text generation | |
model = pipeline("text-generation") | |
# Streamlit app | |
def main(): | |
st.title("Cherry Bot") | |
st.markdown("*Your Sweetest Companion*") | |
# Maintain a list to store text inputs for each chat | |
chat_inputs = [] | |
# Add a sidebar to the app | |
with st.sidebar: | |
st.markdown("# Text Generation Settings") | |
# Slider for adjusting the maximum length of generated text | |
max_length = st.slider("Max Length of Generated Text:", min_value=10, max_value=200, value=50, step=10) | |
# Add a "New Chat" button | |
if st.button("New Chat"): | |
chat_inputs.append(st.text_area(f"Enter starting text for Chat {len(chat_inputs) + 1}:", height=100)) | |
# Perform text generation for each chat | |
for idx, starting_text in enumerate(chat_inputs): | |
st.header(f"Chat {idx + 1}") | |
if st.button(f"Generate Text for Chat {idx + 1}"): | |
if starting_text: | |
# Generate text using the loaded model | |
generated_text = model(starting_text, max_length=max_length)[0]['generated_text'] | |
# Display the generated text | |
st.write("Generated Text:") | |
st.write(generated_text) | |
if __name__ == "__main__": | |
main() | |