Create app.py

app.py

@@ -0,0 +1,152 @@
+import gradio as gr
+import requests
+from transformers import pipeline
+from langdetect import detect
+import pandas as pd
+import matplotlib.pyplot as plt
+import os
+
+HF_TOKEN = os.getenv("HF_TOKEN")
+
+# Fonction pour appeler l'API Zephyr
+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, "parameters": {"max_new_tokens": 300}}
+
+    try:
+        response = requests.post(API_URL, headers=headers, json=payload, timeout=60)
+        response.raise_for_status()
+        return response.json()[0]["generated_text"]
+    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"""
+    You are a professional financial analyst AI.
+
+    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.
+    """
+
+    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, 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")
+        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, None]
+        )
+
+        download_btn.click(
+            download_history,
+            inputs=[history],
+            outputs=[download_file]
+        )
+
+    iface.launch()
+
+if __name__ == "__main__":
+    launch_app()