app.py

import gradio as gr
from transformers import pipeline
from langdetect import detect
import pandas as pd
import textstat

# Chargement du modèle de sentiment
classifier = pipeline(
    "sentiment-analysis",
    model="mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis"
)

# Modèles de traduction
translator_to_en = pipeline("translation", model="Helsinki-NLP/opus-mt-mul-en")
translator_to_fr = pipeline("translation", model="Helsinki-NLP/opus-mt-en-fr")

# Modèle explicatif CPU-friendly
explainer = pipeline("text2text-generation", model="facebook/blenderbot-1B-distill")

# Fonction pour suggérer le meilleur modèle
def suggest_model(text):
    word_count = len(text.split())
    if word_count < 50:
        return "Rapide"
    elif word_count <= 200:
        return "Équilibré"
    else:
        return "Précis"

# Fonction d'analyse
def full_analysis(text, mode, detail_mode, count, history):
    if not text:
        return "Entrez une phrase.", "", "", 0, history, None

    try:
        lang = detect(text)
    except:
        lang = "unknown"

    if lang != "en":
        text = translator_to_en(text, max_length=512)[0]['translation_text']

    result = classifier(text)[0]
    sentiment_output = f"Sentiment : {result['label']} (Score: {result['score']:.2f})"

    prompt = f"""
    You are a financial analyst AI.
    Based on the following financial news: \"{text}\",
    explain clearly why the sentiment is {result['label'].lower()}.
    {"Write a concise paragraph." if detail_mode == "Normal" else "Write a detailed explanation over multiple paragraphs."}
    """

    explanation_en = explainer(prompt, max_length=300 if detail_mode == "Expert" else 150)[0]['generated_text']
    explanation_fr = translator_to_fr(explanation_en, max_length=512)[0]['translation_text']

    clarity_score = textstat.flesch_reading_ease(explanation_en)

    count += 1
    history.append({
        "Texte": text,
        "Sentiment": result['label'],
        "Score": f"{result['score']:.2f}",
        "Explication_EN": explanation_en,
        "Explication_FR": explanation_fr,
        "Clarté": f"{clarity_score:.1f}"
    })

    return sentiment_output, explanation_en, explanation_fr, clarity_score, count, history, None

# Fonction pour télécharger historique CSV
def download_history(history):
    if not history:
        return None
    df = pd.DataFrame(history)
    file_path = "/tmp/analysis_history.csv"
    df.to_csv(file_path, index=False)
    return file_path

# Interface Gradio
def launch_app():
    with gr.Blocks(theme=gr.themes.Base(), css="body {background-color: #0D1117; color: white;} .gr-button {background-color: #161B22; border: 1px solid #30363D;}") as iface:

        gr.Markdown("# 📈 Analyse Financière Premium + Explication IA", elem_id="title")
        gr.Markdown("Entrez une actualité financière. L'IA analyse et explique en anglais/français. Choisissez votre mode d'explication.")

        count = gr.State(0)
        history = gr.State([])

        with gr.Row():
            input_text = gr.Textbox(lines=4, placeholder="Entrez une actualité ici...", label="Texte à analyser")

        with gr.Row():
            mode_selector = gr.Dropdown(
                choices=["Rapide", "Équilibré", "Précis"],
                value="Équilibré",
                label="Mode recommandé selon la taille"
            )
            detail_mode_selector = gr.Dropdown(
                choices=["Normal", "Expert"],
                value="Normal",
                label="Niveau de détail"
            )

        analyze_btn = gr.Button("Analyser")
        download_btn = gr.Button("Télécharger CSV")

        with gr.Row():
            sentiment_output = gr.Textbox(label="Résultat du Sentiment")

        with gr.Row():
            with gr.Column():
                explanation_output_en = gr.Textbox(label="Explication en Anglais")
            with gr.Column():
                explanation_output_fr = gr.Textbox(label="Explication en Français")

        clarity_score_output = gr.Textbox(label="Score de Clarté (Flesch Reading Ease)")
        download_file = gr.File(label="Fichier CSV")

        input_text.change(lambda t: gr.update(value=suggest_model(t)), inputs=[input_text], outputs=[mode_selector])

        analyze_btn.click(
            full_analysis,
            inputs=[input_text, mode_selector, detail_mode_selector, count, history],
            outputs=[sentiment_output, explanation_output_en, explanation_output_fr, clarity_score_output, count, history, download_file]
        )

        download_btn.click(
            download_history,
            inputs=[history],
            outputs=[download_file]
        )

    iface.launch()

if __name__ == "__main__":
    launch_app()