import streamlit as st import torch import numpy as np import pandas as pd from PIL import Image import nltk from transformers import AutoModelForCausalLM, AutoTokenizer import yaml # Download NLTK data nltk.download('punkt') # Load YAML configuration @st.cache def load_yaml(file_path): with open(file_path, "r") as file: return yaml.safe_load(file) config = load_yaml("self-evolving-agent-prompt-en.yaml.txt") # Load model and tokenizer @st.cache(allow_output_mutation=True) def load_model_and_tokenizer(model_path): tokenizer = AutoTokenizer.from_pretrained("gpt2") model = AutoModelForCausalLM.from_pretrained( "gpt2", state_dict=torch.load(model_path, map_location=torch.device("cpu")), ) return tokenizer, model tokenizer, model = load_model_and_tokenizer("flux_lustly-ai_v1.safetensors") # Streamlit UI setup st.set_page_config(page_title="NOVA Assistant", layout="wide") st.title("NOVA Assistant") st.markdown(config.get("description", "An advanced AI assistant.")) # User input user_input = st.text_input("Enter your question or prompt:") if user_input: with st.spinner("Processing..."): # Use NLTK to preprocess the input sentences = nltk.sent_tokenize(user_input) word_count = sum(len(nltk.word_tokenize(sentence)) for sentence in sentences) # Display preprocessing stats st.write(f"Preprocessing stats: {len(sentences)} sentences, {word_count} words") # Generate AI response prompt_template = config.get("prompt_template", "{input}") prompt = prompt_template.replace("{input}", user_input) inputs = tokenizer(prompt, return_tensors="pt") outputs = model.generate(inputs.input_ids, max_length=150, num_return_sequences=1) response = tokenizer.decode(outputs[0], skip_special_tokens=True) # Example: Use numpy for a dummy operation (e.g., scaling output length) response_length = len(response.split()) scaled_length = np.sqrt(response_length) # Example use of numpy st.write(f"Response length (scaled): {scaled_length:.2f}") st.subheader("AI Response:") st.write(response) # Adding a sample DataFrame with Pandas st.sidebar.header("Sample Data") data = { "Input Length": [5, 10, 20], "Response Length": [15, 25, 35], "AI Confidence": [0.8, 0.9, 0.95] } df = pd.DataFrame(data) st.sidebar.write("Sample DataFrame:") st.sidebar.dataframe(df) # Image processing with Pillow (optional) uploaded_file = st.file_uploader("Upload an image (optional):", type=["png", "jpg", "jpeg"]) if uploaded_file: img = Image.open(uploaded_file) st.image(img, caption="Uploaded Image", use_column_width=True) st.write(f"Image Size: {img.size} (Width x Height)")