app.py

import streamlit as st
import pandas as pd
from collections import Counter
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity

class SmartBubble:
    def __init__(self):
        self.keywords = Counter()
        self.liked_content = []
        self.vectorizer = TfidfVectorizer(stop_words='english')
    
    def absorb_content(self, content, is_liked=True):
        """Absorb content and extract its key features"""
        # Extract words and update keyword counter
        words = content.lower().split()
        if is_liked:
            self.keywords.update(words)
            self.liked_content.append(content)
    
    def get_similarity_score(self, new_content):
        """Calculate similarity between new content and liked content"""
        if not self.liked_content:
            return 0
        
        # Combine liked content into one document
        all_content = self.liked_content + [new_content]
        
        # Calculate TF-IDF vectors
        tfidf_matrix = self.vectorizer.fit_transform(all_content)
        
        # Calculate average similarity with all liked content
        similarities = cosine_similarity(
            tfidf_matrix[-1:],  # New content vector
            tfidf_matrix[:-1]   # Liked content vectors
        )
        return float(similarities.mean())

# Streamlit app
st.title("Smart Bubble Protocol")

# Initialize session state
if 'smart_bubble' not in st.session_state:
    st.session_state.smart_bubble = SmartBubble()
if 'content_list' not in st.session_state:
    st.session_state.content_list = []

# Input section
st.header("Add New Content")
new_content = st.text_area("Enter content to analyze:")
if st.button("Add to Content List"):
    if new_content:
        st.session_state.content_list.append(new_content)
        st.success("Content added!")

# Like/Training section
st.header("Train Your Bubble")
for i, content in enumerate(st.session_state.content_list):
    col1, col2 = st.columns([4, 1])
    with col1:
        st.text_area(f"Content {i+1}", content, height=100, key=f"content_{i}")
    with col2:
        if st.button("Like", key=f"like_{i}"):
            st.session_state.smart_bubble.absorb_content(content)
            st.success("Content absorbed into bubble!")

# Analysis section
st.header("Smart Bubble Analysis")
if st.session_state.content_list:
    analysis_data = []
    for content in st.session_state.content_list:
        score = st.session_state.smart_bubble.get_similarity_score(content)
        keywords = " ".join(word for word, _ in 
            Counter(content.lower().split()).most_common(5))
        analysis_data.append({
            'Content Preview': content[:100] + "...",
            'Similarity Score': f"{score:.2%}",
            'Top Keywords': keywords
        })
    
    # Create and display analysis DataFrame
    df = pd.DataFrame(analysis_data)
    df = df.sort_values('Similarity Score', ascending=False)
    st.dataframe(df)

# Bubble insights
st.header("Bubble Insights")
if st.session_state.smart_bubble.keywords:
    top_keywords = st.session_state.smart_bubble.keywords.most_common(10)
    st.write("Top keywords in your bubble:")
    for word, count in top_keywords:
        st.write(f"- {word}: {count}")