Create app.py

app.py

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+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)")
+