app.py

import gradio as gr
from transformers import pipeline
from langdetect import detect
from huggingface_hub import InferenceClient
import pandas as pd
import os

HF_TOKEN = os.getenv("HF_TOKEN")

# Fonction pour appeler l'API Zephyr
def call_zephyr_api(prompt, hf_token=HF_TOKEN):
    client = InferenceClient("HuggingFaceH4/zephyr-7b-beta", token=hf_token)
    try:
        response = client.text_generation(prompt, max_new_tokens=300)
        return response
    except Exception as e:
        raise gr.Error(f"❌ Erreur d'appel API Hugging Face : {str(e)}")

# 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")

# 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"""<|system|>
You are a professional financial analyst AI.
</s>
<|user|>
Analyze the following financial news carefully:
"{text}"

The detected sentiment for this news is: {result['label'].lower()}.

Now, explain why the sentiment is {result['label'].lower()} using a logical, fact-based explanation.
Base your reasoning only on the given news text.
Do not repeat the news text or the prompt.
Respond only with your financial analysis in one clear paragraph.
Write in a clear and professional tone.
</s>
<|assistant|>"""
    explanation_en = call_zephyr_api(prompt)
    explanation_fr = translator_to_fr(explanation_en, max_length=512)[0]['translation_text']

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

    return sentiment_output, explanation_en, explanation_fr, count, history

# 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")
        reset_graph_btn = gr.Button("Reset Graphique")
        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")

        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, count, history]
        )

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

    iface.launch()

if __name__ == "__main__":
    launch_app()