stuff.py

import streamlit as st
import requests
import pandas as pd
import matplotlib.pyplot as plt
from textblob import TextBlob
from datetime import datetime
from io import BytesIO
from fpdf import FPDF

# --- CONFIGURATION ---
OPENWEATHER_API_KEY = "YOUR_API_KEY"  # Remplace avec ta clé OpenWeatherMap

# --- FONCTIONS ---
def get_air_quality(city):
    url = f"http://api.openweathermap.org/data/2.5/air_pollution?appid={OPENWEATHER_API_KEY}&q={city}"
    geocode_url = f"http://api.openweathermap.org/geo/1.0/direct?q={city}&limit=1&appid={OPENWEATHER_API_KEY}"
    geo_res = requests.get(geocode_url).json()
    if not geo_res:
        return None
    lat, lon = geo_res[0]['lat'], geo_res[0]['lon']
    air_url = f"http://api.openweathermap.org/data/2.5/air_pollution?lat={lat}&lon={lon}&appid={OPENWEATHER_API_KEY}"
    res = requests.get(air_url)
    if res.status_code == 200:
        return res.json()['list'][0]
    return None

def fake_social_comments(city):
    return [
        f"The air in {city} is terrible today!",
        f"I love how walkable {city} is.",
        f"Too much traffic in downtown {city}...",
        f"Great use of solar energy in {city}!",
        f"{city} needs more bike lanes."
    ]

def analyze_sentiment(comments):
    results = []
    for comment in comments:
        blob = TextBlob(comment)
        sentiment = blob.sentiment.polarity
        results.append({"comment": comment, "sentiment": sentiment})
    return results

def summarize_report(air_data, sentiment_data):
    avg_sentiment = sum(d['sentiment'] for d in sentiment_data) / len(sentiment_data)
    quality_index = air_data['main']['aqi'] if air_data else 'Unknown'
    if avg_sentiment > 0.3:
        mood = "Generally positive social feedback."
    elif avg_sentiment < -0.3:
        mood = "Widespread dissatisfaction in social media."
    else:
        mood = "Mixed or neutral sentiments."

    report = f"Air Quality Index: {quality_index}\nSocial Sentiment Score: {avg_sentiment:.2f}\nAnalysis: {mood}"
    return report

def generate_sentiment_chart(data):
    df = pd.DataFrame(data)
    fig, ax = plt.subplots()
    df['sentiment'].plot(kind='bar', color='skyblue', ax=ax)
    ax.set_title("Sentiment Score per Comment")
    ax.set_xticks(range(len(df)))
    ax.set_xticklabels([f"{i+1}" for i in range(len(df))])
    ax.set_ylabel("Sentiment Polarity")
    st.pyplot(fig)

def generate_pdf_report(city, air_data, sentiment_data, summary):
    pdf = FPDF()
    pdf.add_page()
    pdf.set_font("Arial", size=12)
    pdf.cell(200, 10, txt=f"Ville Durable Report - {city}", ln=True, align='C')
    pdf.ln(10)

    pdf.set_font("Arial", 'B', 12)
    pdf.cell(200, 10, txt="Résumé de l'analyse:", ln=True)
    pdf.set_font("Arial", size=11)
    pdf.multi_cell(0, 10, summary)
    pdf.ln(5)

    pdf.set_font("Arial", 'B', 12)
    pdf.cell(200, 10, txt="Commentaires sociaux:", ln=True)
    pdf.set_font("Arial", size=11)
    for item in sentiment_data:
        pdf.multi_cell(0, 10, f"{item['comment']} (Score: {item['sentiment']:.2f})")

    buffer = BytesIO()
    pdf.output(buffer)
    buffer.seek(0)
    return buffer

# --- STREAMLIT APP ---
st.set_page_config(page_title="Ville Durable Dashboard", layout="centered")
st.title("🌍 Ville Durable Dashboard")

city = st.text_input("Entrez le nom d'une ville:", value="Paris")

if st.button("Analyser"):
    with st.spinner("Collecte des données..."):
        air_data = get_air_quality(city)
        comments = fake_social_comments(city)
        sentiment_data = analyze_sentiment(comments)
        summary = summarize_report(air_data, sentiment_data)

    st.subheader("📋 Rapport Résumé")
    st.code(summary)

    st.subheader("📊 Graphique des sentiments")
    generate_sentiment_chart(sentiment_data)

    st.subheader("💬 Commentaires sociaux analysés")
    for d in sentiment_data:
        st.write(f"{d['comment']} (Sentiment: {d['sentiment']:.2f})")

    pdf = generate_pdf_report(city, air_data, sentiment_data, summary)
    st.download_button(
        label="📥 Télécharger le rapport PDF",
        data=pdf,
        file_name=f"rapport_{city.lower()}.pdf",
        mime="application/pdf"
    )