Hugging Face's logo

Spaces:
DinoFrog
/
sentiment-analysis-test
Sleeping

App Files Community
sentiment-analysis-test
File size: 5,157 Bytes 
12fb52c
 
 
73da356
12fb52c
 
 
 
 
 
73da356
 
12fb52c
73da356
 
12fb52c
6741a19
12fb52c
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
73da356
 
 
 
 
12fb52c
73da356
12fb52c
 
 
 
 
 
 
 
 
 
 
abc7d26
12fb52c
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
031341e
12fb52c
 
 
 
 
 
 
 
 
 
 
6741a19
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
 
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(messages, hf_token=HF_TOKEN):
    client = InferenceClient("HuggingFaceH4/zephyr-7b-beta", token=hf_token)
    try:
        response = client.chat_completion(messages, max_tokens=300)
        return response.choices[0].message.content
    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})"

    messages = [
        {"role": "system", "content": "You are a professional financial analyst AI."},
        {"role": "user", "content": f"Analyze the following financial news carefully:\n\"{text}\"\n\nThe detected sentiment for this news is: {result['label'].lower()}.\n\nNow, explain why the sentiment is {result['label'].lower()} using a logical, fact-based explanation.\nBase your reasoning only on the given news text.\nDo not repeat the news text or the prompt.\nRespond only with your financial analysis in one clear paragraph.\nWrite in a clear and professional tone."}
    ]
    explanation_en = call_zephyr_api(messages)

    explanation_en = call_zephyr_api(messages)
    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()