app.py

import streamlit as st
import nltk
import string
import re
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
from faqs import faqs

nltk.download('punkt', quiet=True)
nltk.download('punkt_tab', quiet=True)
nltk.download('stopwords', quiet=True)

st.set_page_config(
    page_title="ShredBot — Fitness FAQ Chatbot",
    page_icon="💪",
    layout="centered"
)

st.markdown("""
<style>
    @import url('https://fonts.googleapis.com/css2?family=Syne:wght@400;500;600;700&family=Inter:wght@300;400;500&display=swap');

    html, body, [class*="css"] {
        font-family: 'Inter', sans-serif;
    }

    .stApp {
        background-color: #0a0a0a;
    }

    .main-title {
        font-family: 'Syne', sans-serif;
        font-size: 2.6rem;
        font-weight: 700;
        color: #f5f5f5;
        text-align: center;
        margin-bottom: 0.2rem;
        letter-spacing: -1px;
    }

    .accent {
        color: #a3e635;
    }

    .subtitle {
        text-align: center;
        color: #6b7280;
        font-size: 0.95rem;
        margin-bottom: 2rem;
        font-weight: 300;
    }

    .suggestion-label {
        font-size: 0.75rem;
        color: #6b7280;
        text-transform: uppercase;
        letter-spacing: 0.1em;
        margin-bottom: 0.5rem;
        text-align: center;
    }

    .stChatMessage {
        background: transparent !important;
    }

    [data-testid="stDecoration"] {
        display: none;
    }

    header[data-testid="stHeader"] {
        background: none;
    }

    .confidence-bar {
        height: 3px;
        background: #1a1a1a;
        border-radius: 2px;
        margin-top: 6px;
    }

    .stButton > button {
        background: #1a1a1a !important;
        color: #a3e635 !important;
        border: 1px solid #2a2a2a !important;
        border-radius: 20px !important;
        font-size: 0.78rem !important;
        font-family: 'Inter', sans-serif !important;
        padding: 0.3rem 1rem !important;
        transition: all 0.2s ease !important;
    }

    .stButton > button:hover {
        background: #a3e635 !important;
        color: #0a0a0a !important;
        border-color: #a3e635 !important;
    }

    [data-testid="stChatInputTextArea"] {
        background: #111111 !important;
        color: #f5f5f5 !important;
        border: 1px solid #2a2a2a !important;
        border-radius: 12px !important;
        font-family: 'Inter', sans-serif !important;
    }
</style>
""", unsafe_allow_html=True)

def preprocess(text):
    text = text.lower()
    text = re.sub(r'[^\w\s]', '', text)
    tokens = word_tokenize(text)
    stop_words = set(stopwords.words('english'))
    tokens = [t for t in tokens if t not in stop_words]
    return ' '.join(tokens)

def get_best_answer(user_question, threshold=0.11):
    faq_questions = [f['question'] for f in faqs]
    processed_faqs = [preprocess(q) for q in faq_questions]
    processed_user = preprocess(user_question)

    corpus = processed_faqs + [processed_user]
    vectorizer = TfidfVectorizer()
    tfidf_matrix = vectorizer.fit_transform(corpus)

    user_vector = tfidf_matrix[-1]
    faq_vectors = tfidf_matrix[:-1]

    similarities = cosine_similarity(user_vector, faq_vectors)[0]
    best_idx = similarities.argmax()
    best_score = similarities[best_idx]

    if best_score < threshold:
        return None, 0

    return faqs[best_idx]['answer'], float(best_score)

st.markdown('<div class="main-title">Shred<span class="accent">Bot</span> 💪</div>', unsafe_allow_html=True)
st.markdown('<div class="subtitle">Your AI-powered fitness & nutrition assistant</div>', unsafe_allow_html=True)

SUGGESTIONS = [
    "How much protein do I need?",
    "Best time to work out?",
    "Is creatine safe?",
    "How do I get abs?",
    "What should I eat after gym?",
]

if "messages" not in st.session_state:
    st.session_state.messages = [
        {
            "role": "assistant",
            "content": "Hey! I'm ShredBot 💪 Ask me anything about fitness, nutrition, workouts, or supplements."
        }
    ]

for message in st.session_state.messages:
    with st.chat_message(message["role"]):
        st.markdown(message["content"])

st.markdown('<div class="suggestion-label">Try asking</div>', unsafe_allow_html=True)
cols = st.columns(len(SUGGESTIONS))
for i, suggestion in enumerate(SUGGESTIONS):
    with cols[i]:
        if st.button(suggestion, key=f"suggestion_{i}"):
            st.session_state.messages.append({"role": "user", "content": suggestion})
            answer, score = get_best_answer(suggestion)
            if answer:
                st.session_state.messages.append({"role": "assistant", "content": answer})
            else:
                st.session_state.messages.append({
                    "role": "assistant",
                    "content": "Hmm, I don't have a good answer for that. Try rephrasing or ask something about workouts, nutrition, or supplements!"
                })
            st.rerun()

if prompt := st.chat_input("Ask about fitness, nutrition, workouts..."):
    st.session_state.messages.append({"role": "user", "content": prompt})

    with st.chat_message("user"):
        st.markdown(prompt)

    with st.chat_message("assistant"):
        answer, score = get_best_answer(prompt)
        if answer:
            st.markdown(answer)
            st.session_state.messages.append({"role": "assistant", "content": answer})
        else:
            fallback = "I don't have a specific answer for that. Try asking about protein intake, workout splits, supplements, fat loss, or muscle building!"
            st.markdown(fallback)
            st.session_state.messages.append({"role": "assistant", "content": fallback})