app.py

import gradio as gr
import requests
import pandas as pd
import textstat
import os

# Récupération du token Hugging Face
HF_TOKEN = os.getenv("HF_TOKEN")

# Fonction pour appeler l'API Zephyr-7B
def call_zephyr_api(prompt, hf_token=HF_TOKEN):
    API_URL = "https://api-inference.huggingface.co/models/HuggingFaceH4/zephyr-7b-beta"
    headers = {"Authorization": f"Bearer {hf_token}"}
    payload = {"inputs": prompt}

    try:
        response = requests.post(API_URL, headers=headers, json=payload, timeout=60)
        response.raise_for_status()
        return response.json()[0]["generated_text"].strip()
    except Exception as e:
        raise gr.Error(f"❌ Erreur d'appel API Hugging Face : {str(e)}")

# Fonction principale d'analyse
def full_analysis(text, history):
    if not text:
        return "Entrez une phrase.", "", 0, history, 0

    # 1. Demander à Zephyr de détecter le sentiment
    prompt_sentiment = f"""
You are a financial news sentiment detector.

Given the following news text:
\"{text}\"

Respond only with one word: positive, neutral, or negative.

Do not add any explanation or extra text.
"""
    detected_sentiment = call_zephyr_api(prompt_sentiment).lower()

    if detected_sentiment not in ["positive", "neutral", "negative"]:
        detected_sentiment = "neutral"

    # 2. Demander à Zephyr d'expliquer
    prompt_explanation = f"""
You are a financial analyst AI.

Given the following financial news:
\"{text}\"

The detected sentiment is: {detected_sentiment}.

Now explain clearly why the sentiment is {detected_sentiment}.
Write a concise paragraph.
"""
    explanation = call_zephyr_api(prompt_explanation)

    # 3. Calculer la clarté
    clarity_score = textstat.flesch_reading_ease(explanation)
    clarity_score = max(0, min(clarity_score, 100))  # Limité entre 0-100

    # 4. Sauvegarder dans l'historique
    history.append({
        "Texte": text,
        "Sentiment": detected_sentiment.capitalize(),
        "Clarté": f"{clarity_score:.1f}",
        "Explication": explanation
    })

    return detected_sentiment.capitalize(), explanation, clarity_score, history, int(clarity_score)

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

# Gradio Interface
def launch_app():
    with gr.Blocks() as iface:
        gr.Markdown("# 📈 Analyse Financière Premium - Zephyr7B")

        with gr.Row():
            input_text = gr.Textbox(label="Entrez votre question financière", lines=3)

        with gr.Row():
            analyze_btn = gr.Button("Analyser")
            download_btn = gr.Button("Télécharger l'historique")

        sentiment_output = gr.Textbox(label="Sentiment Détecté")
        explanation_output = gr.Textbox(label="Explication de l'IA", lines=5)
        clarity_score_text = gr.Textbox(label="Score de Clarté (%)")
        clarity_slider = gr.Slider(0, 100, label="Clarté (%)", interactive=False)
        file_output = gr.File(label="Fichier CSV")

        history = gr.State([])

        analyze_btn.click(
            full_analysis,
            inputs=[input_text, history],
            outputs=[sentiment_output, explanation_output, clarity_score_text, history, clarity_slider]
        )

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

    iface.launch()

if __name__ == "__main__":
    launch_app()