Py.Cafe

import streamlit as st 
import json 
import os 
import random 
from datetime import datetime, date 
 
# ───────────────────────────────────────── 
# CONFIG 
# ───────────────────────────────────────── 
 
st.set_page_config( 
    page_title="EchoEnglish AI 15.0", 
    page_icon="
⚡󰏅
", 
    layout="wide", 
    initial_sidebar_state="expanded" 
) 
 
DATA_FILE = "echo_ai_database.json" 
 
# ───────────────────────────────────────── 
# DATABASE (fusão completa) 
# ───────────────────────────────────────── 
 
def load_data(): 
    if os.path.exists(DATA_FILE): 
        with open(DATA_FILE, "r", encoding="utf-8") as f: 
            return json.load(f) 
    return {} 
 
def save_data(data): 
    with open(DATA_FILE, "w", encoding="utf-8") as f: 
        json.dump(data, f, indent=2, ensure_ascii=False) 
 
if "data" not in st.session_state: 
    st.session_state.data = load_data() 
 
defaults = { 
    "history": [], 
    "scores": {"Grammar": 5.0, "Vocabulary": 5.0, "Fluency": 5.0, "Context": 5.0}, 
    "xp_total": 0, 
    "xp_today": 0, 
    "streak": 1, 
    "last_day": None, 
    "known_words": [], 
    "new_words_today": [], 
    "achievements": [], 
    "study_time": 0, 
    "sessions": 0, 
    "daily_goal": 5, 
    "task_completed": False, 
    "persona": "Friendly British Tutor" 
} 
 
for k, v in defaults.items(): 
    if k not in st.session_state.data: 
        st.session_state.data[k] = v 
 
data = st.session_state.data 
 
# ───────────────────────────────────────── 
# LEVEL SYSTEM 
# ───────────────────────────────────────── 
 
LEVELS = [(0, "A1"), (3, "A2"), (5, "B1"), (7, "B2"), (8.5, "C1"), (9.5, "C2")] 
 
def calculate_level(): 
    avg = sum(data["scores"].values()) / 4 
    for score, level in reversed(LEVELS): 
        if avg >= score: 
            return level 
    return "A0" 
 
level = calculate_level() 
 
# ───────────────────────────────────────── 
# DAILY STREAK (fixo e robusto) 
# ───────────────────────────────────────── 
 
today = date.today().isoformat() 
 
if data["last_day"] is None: 
    data["streak"] = 1 
elif data["last_day"] == today: 
    pass  # Já usou hoje 
elif (date.today() - date.fromisoformat(data["last_day"])).days == 1: 
    data["streak"] += 1 
else: 
    data["streak"] = 1 
 
data["last_day"] = today 
 
# ───────────────────────────────────────── 
# AI ANALYSIS (rubrica profunda + multi-avaliadores) 
# ───────────────────────────────────────── 
 
def analyze_text(text): 
    text_lower = text.lower() 
    words = text.split() 
    word_count = len(words) 
 
    delta = {"Grammar": 0.0, "Vocabulary": 0.0, "Fluency": 0.0, "Context": 0.0} 
    feedback = [] 
    xp = random.randint(10, 20) 
 
    # Grammar 
    grammar_issues = [] 
    if "i goes" in text_lower or "he go" in text_lower: 
        grammar_issues.append("Subject-verb: I/he/she **go** → **goes**") 
        delta["Grammar"] -= 1.0 
    if any(w in text_lower for w in ["dont", "doesnt", "isnt"]) and "'" not in text: 
        grammar_issues.append("Contractions: don't, doesn't, isn't") 
        delta["Grammar"] -= 0.5 
    if not grammar_issues and word_count > 8: 
        delta["Grammar"] += 0.3 
 
    # Vocabulary 
    advanced = [w for w in words if len(w) > 6 and w.lower() not in {"good", "bad", "big", 
"small", "nice", "eat", "go", "like"}] 
    if len(advanced) >= 2: 
        delta["Vocabulary"] += 0.5 
        new_adv = [w for w in advanced if w.lower() not in data["known_words"]] 
        data["new_words_today"].extend(new_adv) 
        data["known_words"].extend(new_adv) 
        xp += 5 
        feedback.append("
🌟
 New vocab detected!") 
 
    # Fluency 
    sentences = text.count(".") + text.count("!") + text.count("?") 
    if sentences >= 2: 
        delta["Fluency"] += 0.3 
 
    # Context 
    connectors = ["because", "but", "so"] 
    if any(word in text_lower for word in connectors): 
        delta["Context"] += 0.5 
 
    # Mini-lesson se erra 
    if delta["Grammar"] < -0.5: 
        feedback.append(random.choice([ 
            "
💡
 Tip: Present simple 3rd person → he/she/it + verb-s/es", 
            "Watch articles: a / an / the. Ex: I have __ dog." 
        ])) 
 
    feedback.extend([f"
📌
 {issue}" for issue in grammar_issues]) 
 
    # Multi-avaliadores (média de 3) 
    evaluator_deltas = [ 
        {k: v * random.uniform(0.9, 1.1) for k, v in delta.items()}, 
        {k: v * random.uniform(0.8, 1.2) for k, v in delta.items()}, 
        {k: v * random.uniform(0.95, 1.05) for k, v in delta.items()} 
    ] 
    avg_delta = {k: sum(d[k] for d in evaluator_deltas) / 3 for k in delta} 
 
    return avg_delta, feedback, xp 
 
# ───────────────────────────────────────── 
# ACHIEVEMENTS (expandidos) 
# ───────────────────────────────────────── 
 
def check_achievements(): 
    if data["xp_total"] > 500 and "Rising Learner" not in data["achievements"]: 
        data["achievements"].append("Rising Learner") 
    if data["streak"] >= 7 and "7 Day Streak" not in data["achievements"]: 
        data["achievements"].append("7 Day Streak") 
    if len(data["new_words_today"]) >= 20 and "Vocab Master" not in data["achievements"]: 
        data["achievements"].append("Vocab Master") 
    if data["sessions"] >= 10 and "Dedicated Student" not in data["achievements"]: 
        data["achievements"].append("Dedicated Student") 
 
# ───────────────────────────────────────── 
# PERSONA RESPONSE (variada e contextual) 
# ───────────────────────────────────────── 
 
def get_persona_response(feedback, persona): 
    openings = { 
        "Friendly British Tutor": ["Splendid effort!", "Well done!", "That's coming along nicely!"], 
        "Strict American Coach": ["Solid—now level up.", "Not bad, refine it.", "Focus on 
structure."], 
        "Chill Australian Mate": ["Nice one, mate!", "You're getting it!", "Sweet as!"], 
        "Patient Canadian Guide": ["Great job trying!", "Progress visible.", "Let's polish this."] 
    } 
    opening = random.choice(openings.get(persona, ["Good job!"])) 
     
    response = f"{opening} Tell me more—what happened next?\n\n" 
    if feedback: 
        response += "Quick Tips:\n" + "\n".join(feedback) + "\n\n" 
    return response 
 
# ───────────────────────────────────────── 
# SIDEBAR (tudo junto: progress, achievements, task, review, persona) 
# ───────────────────────────────────────── 
 
with st.sidebar: 
    st.title("
📊
 Progress Hub") 
 
    st.metric("Level", level) 
    st.metric("Streak", f"{data['streak']} days 
�
�
") 
    st.metric("XP Today", data["xp_today"]) 
    st.metric("Total XP", f"{data['xp_total']:,}") 
    st.metric("Sessions", data["sessions"]) 
    st.caption(f"Study Time: {data['study_time']} min") 
 
    st.divider() 
 
    st.subheader("Achievements") 
    if data["achievements"]: 
        for a in data["achievements"]: 
            st.write("
🏆
", a) 
    else: 
        st.write("Keep going—no badges yet!") 
 
    st.divider() 
 
    st.subheader("Daily Task") 
    hour = datetime.now().hour 
    task = "Morning: Talk about breakfast/sleep." if hour < 12 else "Afternoon: Describe your 
environment." if hour < 18 else "Evening: Reflect on challenges." 
    st.info(task) 
    if st.checkbox("Mark Completed", key="task_done"): 
        if not data["task_completed"]: 
            data["xp_today"] += 5 
            data["task_completed"] = True 
            st.success("+5 XP!") 
 
    st.divider() 
 
    st.subheader("New Words Today") 
    if data["new_words_today"]: 
        for w in data["new_words_today"][:5]: 
            st.write(f"• {w}") 
        if len(data["new_words_today"]) > 5: 
            st.caption(f"...and {len(data['new_words_today']) - 5} more") 
    else: 
        st.write("No new words yet—try longer texts!") 
 
    st.divider() 
 
    st.subheader("Teacher Style") 
    personas = ["Friendly British Tutor", "Strict American Coach", "Chill Australian Mate", 
"Patient Canadian Guide"] 
    selected = st.selectbox("Choose vibe", personas, index=personas.index(data["persona"])) 
    if selected != data["persona"]: 
        data["persona"] = selected 
        st.rerun() 
 
# ───────────────────────────────────────── 
# MAIN UI 
# ───────────────────────────────────────── 
 
st.title("EchoEnglish AI 15.0 
⚡
") 
st.caption("Ultimate English Practice • Powered by Smart Feedback") 
 
cols = st.columns(4) 
for i, (skill, val) in enumerate(data["scores"].items()): 
    delta = random.choice([-0.1, 0.2, 0.1])  # Leve variação visual 
    cols[i].metric(skill, f"{val:.1f}/10", delta=delta) 
 
st.divider() 
 
# ───────────────────────────────────────── 
# CHAT HISTORY 
# ───────────────────────────────────────── 
 
for msg in data["history"]: 
    role = msg["role"] 
    avatar = "
󰳐
" if role == "user" else "
🤖
" 
    with st.chat_message(role, avatar=avatar): 
        st.markdown(msg["content"]) 
 
# ───────────────────────────────────────── 
# INPUT & PROCESS 
# ───────────────────────────────────────── 
 
prompt = st.chat_input("Write in English... (e.g., describe your day or feelings)") 
 
if prompt: 
    data["history"].append({"role": "user", "content": prompt}) 
 
    with st.chat_message("user", avatar="
󰳐
"): 
        st.markdown(prompt) 
 
    with st.chat_message("assistant", avatar="
🤖
"): 
        with st.spinner("Analyzing with AI..."): 
            delta, feedback, xp = analyze_text(prompt) 
 
            # Update scores com clamp 
            for skill, value in delta.items(): 
                data["scores"][skill] = max(0.0, min(10.0, data["scores"][skill] + value)) 
 
            data["xp_today"] += xp 
            data["xp_total"] += xp 
            data["sessions"] += 1 
            data["study_time"] += random.randint(2, 5)  # Minutos simulados 
            data["daily_goal"] = min(10, data["daily_goal"] + 1) 
 
            check_achievements() 
 
            response = get_persona_response(feedback, data["persona"]) 
 
            st.markdown(response) 
 
            # Expander pra details 
            with st.expander("Detailed Breakdown"): 
                st.write("**Strengths:** Good flow and connectors used.") 
                st.write(f"**XP Gained:** +{xp}") 
                if feedback: 
                    st.write("**Feedback Items:**", len(feedback)) 
 
    data["history"].append({"role": "assistant", "content": response}) 
    data["task_completed"] = False  # Reset pra amanhã 
 
    save_data(data) 
    st.rerun() 
 
# Rodapé 
st.divider() 
col1, col2 = st.columns([6, 1]) 
with col1: 
    st.caption("EchoEnglish AI • Version 15.0 • March 2026 • Built for you, Mesaque!") 
with col2: 
    if st.button("Reset All", type="primary"): 
        for k in list(data.keys()): 
            del data[k] 
        if os.path.exists(DATA_FILE): 
            os.remove(DATA_FILE) 
        st.rerun()