Update app.py

app.py

@@ -4,14 +4,32 @@ from langdetect import detect
 from huggingface_hub import InferenceClient
 import pandas as pd
 import os
+import asyncio
+import nltk
+from nltk.tokenize import sent_tokenize
+
+# Téléchargement de punkt_tab avec gestion d'erreur
+try:
+    nltk.download('punkt_tab', download_dir='/usr/local/share/nltk_data')
+except Exception as e:
+    raise Exception(f"Erreur lors du téléchargement de punkt_tab : {str(e)}. Veuillez vérifier votre connexion réseau et les permissions du répertoire /usr/local/share/nltk_data.")
 
 HF_TOKEN = os.getenv("HF_TOKEN")
 
-# Fonction pour appeler l'API Zephyr
-def call_zephyr_api(prompt, hf_token=HF_TOKEN):
+# Fonction pour appeler l'API Zephyr avec des paramètres ajustés
+async def call_zephyr_api(prompt, mode, hf_token=HF_TOKEN):
     client = InferenceClient("HuggingFaceH4/zephyr-7b-beta", token=hf_token)
     try:
-        response = client.text_generation(prompt, max_new_tokens=300)
+        if mode == "Rapide":
+            max_new_tokens = 50
+            temperature = 0.3
+        elif mode == "Équilibré":
+            max_new_tokens = 100
+            temperature = 0.5
+        else:  # Précis
+            max_new_tokens = 150
+            temperature = 0.7
+        response = await asyncio.to_thread(client.text_generation, prompt, max_new_tokens=max_new_tokens, temperature=temperature)
         return response
     except Exception as e:
         raise gr.Error(f"❌ Erreur d'appel API Hugging Face : {str(e)}")
@@ -23,6 +41,18 @@ classifier = pipeline("sentiment-analysis", model="mrm8488/distilroberta-finetun
 translator_to_en = pipeline("translation", model="Helsinki-NLP/opus-mt-mul-en")
 translator_to_fr = pipeline("translation", model="Helsinki-NLP/opus-mt-en-fr")
 
+# Traduction en français avec Helsinki-NLP
+def safe_translate_to_fr(text, max_length=512):
+    try:
+        sentences = sent_tokenize(text)
+        translated_sentences = []
+        for sentence in sentences:
+            translated = translator_to_fr(sentence, max_length=max_length)[0]['translation_text']
+            translated_sentences.append(translated)
+        return " ".join(translated_sentences)
+    except Exception as e:
+        return f"Erreur de traduction : {str(e)}"
+
 # Fonction pour suggérer le meilleur modèle
 def suggest_model(text):
     word_count = len(text.split())
@@ -36,35 +66,30 @@ def suggest_model(text):
 # Fonction pour créer une jauge de sentiment
 def create_sentiment_gauge(sentiment, score):
     score_percentage = score * 100
-    if sentiment.lower() == "neutral":
-        color = "gray"
-    elif sentiment.lower() == "positive":
-        color = "green"
+    color = "#A9A9A9"
+    if sentiment.lower() == "positive":
+        color = "#2E8B57"
     elif sentiment.lower() == "negative":
-        color = "red"
-    else:
-        color = "gray"
+        color = "#DC143C"
 
     html = f"""
     <div style='width: 100%; max-width: 300px; margin: 10px 0;'>
-        <div style='background-color: #e0e0e0; border-radius: 5px; height: 20px; position: relative;'>
-            <div style='background-color: {color}; width: {score_percentage}%; height: 100%; border-radius: 5px;'>
-            </div>
-            <span style='position: absolute; top: 0; left: 50%; transform: translateX(-50%); color: black; font-size: 12px; line-height: 20px;'>
-                {score_percentage:.1f}%
-            </span>
-        </div>
-        <div style='text-align: center; font-size: 14px; margin-top: 5px;'>
-            Sentiment: {sentiment}
+        <div style='background-color: #D3D3D3; border-radius: 5px; height: 20px; position: relative;'>
+            <div style='background-color: {color}; width: {score_percentage}%; height: 100%; border-radius: 5px;'></div>
+            <span style='position: absolute; top: 0; left: 50%; transform: translateX(-50%); font-weight: bold;'>{score_percentage:.1f}%</span>
         </div>
+        <div style='text-align: center; margin-top: 5px;'>Sentiment : {sentiment}</div>
     </div>
     """
     return html
 
 # Fonction d'analyse
-def full_analysis(text, mode, detail_mode, count, history):
+async def full_analysis(text, mode, detail_mode, count, history):
     if not text:
-        return "Entrez une phrase.", "", "", 0, history, None, ""
+        yield "Entrez une phrase.", "", "", "", 0, history, "", "Aucune analyse effectuée."
+        return
+
+    yield "Analyse en cours... (Étape 1 : Détection de la langue)", "", "", "", count, history, "", "Détection de la langue"
 
     try:
         lang = detect(text)
@@ -72,41 +97,35 @@ def full_analysis(text, mode, detail_mode, count, history):
         lang = "unknown"
 
     if lang != "en":
-        text = translator_to_en(text, max_length=512)[0]['translation_text']
+        text_en = translator_to_en(text, max_length=512)[0]['translation_text']
+    else:
+        text_en = text
 
-    # Étape 1 : Poser une question à Zephyr pour prédire l'impact économique
-    prediction_prompt = f"""<|system|>
-You are a professional financial analyst AI with expertise in economic forecasting.
-</s>
-<|user|>
-Given the following question about a potential economic event: "{text}"
+    yield "Analyse en cours... (Étape 2 : Analyse du sentiment)", "", "", "", count, history, "", "Analyse du sentiment"
 
-Assume the event happens (e.g., if the question is "Will the Federal Reserve raise interest rates?", assume they do raise rates). What would be the likely economic impact of this event? Provide a concise explanation in one paragraph, focusing on the potential positive or negative effects on the economy. Do not repeat the question or the prompt in your response.
-</s>
-<|assistant|>"""
-    prediction_response = call_zephyr_api(prediction_prompt)
-
-    # Étape 2 : Analyser le sentiment de la réponse de Zephyr
-    result = classifier(prediction_response)[0]
+    result = await asyncio.to_thread(classifier, text_en)
+    result = result[0]
     sentiment_output = f"Sentiment prédictif : {result['label']} (Score: {result['score']:.2f})"
     sentiment_gauge = create_sentiment_gauge(result['label'], result['score'])
 
-    # Étape 3 : Générer une explication détaillée
+    yield "Analyse en cours... (Étape 3 : Explication IA)", "", "", "", count, history, "", "Génération de l'explication"
+
     explanation_prompt = f"""<|system|>
-You are a professional financial analyst AI.
+You are a professional financial analyst AI with expertise in economic forecasting.
 </s>
 <|user|>
 Given the following question about a potential economic event: "{text}"
 
-Based on your prediction of the economic impact, which is: "{prediction_response}"
-
-The predicted sentiment for this impact is: {result['label'].lower()}.
+The predicted sentiment for this event 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 predicted economic impact. Respond only with your financial analysis in one clear paragraph. Write in a clear and professional tone.
+Assume the event happens. Explain why this event would likely have a {result['label'].lower()} economic impact.
 </s>
 <|assistant|>"""
-    explanation_en = call_zephyr_api(explanation_prompt)
-    explanation_fr = translator_to_fr(explanation_en, max_length=512)[0]['translation_text']
+    explanation_en = await call_zephyr_api(explanation_prompt, mode)
+
+    yield "Analyse en cours... (Étape 4 : Traduction en français)", "", "", "", count, history, "", "Traduction en français"
+
+    explanation_fr = safe_translate_to_fr(explanation_en)
 
     count += 1
     history.append({
@@ -117,9 +136,9 @@ Now, explain why the sentiment is {result['label'].lower()} using a logical, fac
         "Explication_FR": explanation_fr
     })
 
-    return sentiment_output, explanation_en, explanation_fr, count, history, sentiment_gauge
+    yield sentiment_output, text, explanation_en, explanation_fr, count, history, sentiment_gauge, "✅ Analyse terminée."
 
-# Fonction pour télécharger historique CSV
+# Historique CSV
 def download_history(history):
     if not history:
         return None
@@ -128,44 +147,62 @@ def download_history(history):
     df.to_csv(file_path, index=False)
     return file_path
 
-# Interface Gradio
+# Lancement Gradio avec l'interface restaurée
 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 question sur un événement économique. L'IA prédit l'impact et attribue un sentiment (positif, négatif, neutre).")
+    custom_css = """
+    /* CSS restauré à la version précédente, avant les changements esthétiques non demandés */
+    body {
+        background: linear-gradient(135deg, #0A1D37 0%, #1A3C34 100%);
+        font-family: 'Inter', sans-serif;
+        color: #E0E0E0;
+        padding: 20px;
+    }
+    .gr-box {
+        background: #2A4A43 !important;
+        border: 1px solid #FFD700 !important;
+        border-radius: 12px !important;
+        padding: 20px !important;
+        box-shadow: 0px 4px 12px rgba(255, 215, 0, 0.4);
+    }
+    .gr-button {
+        background: linear-gradient(90deg, #FFD700, #D4AF37);
+        color: #0A1D37;
+        font-weight: bold;
+        border: none;
+        border-radius: 8px;
+        padding: 12px 24px;
+        transition: transform 0.2s;
+    }
+    .gr-button:hover {
+        transform: translateY(-2px);
+        box-shadow: 0 6px 12px rgba(255, 215, 0, 0.5);
+    }
+    """
+
+    with gr.Blocks(theme=gr.themes.Base(), css=custom_css) as iface:
+        gr.Markdown("# 📈 Analyse Financière Premium avec IA")
+        gr.Markdown("**Posez une question économique.** L'IA analyse et explique l'impact.")
 
         count = gr.State(0)
         history = gr.State([])
 
         with gr.Row():
-            input_text = gr.Textbox(lines=4, placeholder="Entrez une question ici (ex. 'La Réserve fédérale augmentera-t-elle ses taux d'intérêt avant 2025 ?')", label="Question économique")
-
-        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"
-            )
+            with gr.Column(scale=2):
+                input_text = gr.Textbox(lines=4, label="Votre question économique")
+            with gr.Column(scale=1):
+                mode_selector = gr.Dropdown(choices=["Rapide", "Équilibré", "Précis"], value="Équilibré", label="Mode de réponse")
+                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 Prédictif")
-
-        sentiment_gauge = gr.HTML(label="Jauge de Sentiment")
+        download_btn = gr.Button("Télécharger l'historique")
 
         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")
+            sentiment_output = gr.Textbox(label="Sentiment prédictif")
+            displayed_prompt = gr.Textbox(label="Votre question", interactive=False)
+            explanation_output_en = gr.Textbox(label="Explication en anglais")
+            explanation_output_fr = gr.Textbox(label="Explication en français")
+            sentiment_gauge = gr.HTML()
+            progress_message = gr.Textbox(label="Progression", interactive=False)
 
         download_file = gr.File(label="Fichier CSV")
 
@@ -174,7 +211,7 @@ def launch_app():
         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, sentiment_gauge]
+            outputs=[sentiment_output, displayed_prompt, explanation_output_en, explanation_output_fr, count, history, sentiment_gauge, progress_message]
         )
 
         download_btn.click(
@@ -183,7 +220,7 @@ def launch_app():
             outputs=[download_file]
         )
 
-    iface.launch()
+    iface.launch(share=True)
 
 if __name__ == "__main__":
     launch_app()