Update app.py

app.py

@@ -5,25 +5,26 @@ from huggingface_hub import InferenceClient
 import pandas as pd
 import os
 import nltk
-nltk.download('punkt_tab')  # Changement de 'punkt' à 'punkt_tab'
+import asyncio
+nltk.download('punkt_tab')
 from nltk.tokenize import sent_tokenize
 
 HF_TOKEN = os.getenv("HF_TOKEN")
 
 # Fonction pour appeler l'API Zephyr avec des paramètres ajustés
-def call_zephyr_api(prompt, mode, hf_token=HF_TOKEN):
+async def call_zephyr_api(prompt, mode, hf_token=HF_TOKEN):
     client = InferenceClient("HuggingFaceH4/zephyr-7b-beta", token=hf_token)
     try:
         if mode == "Rapide":
+            max_new_tokens = 50
+            temperature = 0.3
+        elif mode == "Équilibré":
             max_new_tokens = 100
             temperature = 0.5
-        elif mode == "Équilibré":
-            max_new_tokens = 200
-            temperature = 0.7
         else:  # Précis
-            max_new_tokens = 300
-            temperature = 0.9
-        response = client.text_generation(prompt, max_new_tokens=max_new_tokens, temperature=temperature)
+            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)}")
@@ -31,20 +32,14 @@ def call_zephyr_api(prompt, mode, hf_token=HF_TOKEN):
 # Chargement du modèle de sentiment pour analyser les réponses
 classifier = pipeline("sentiment-analysis", model="mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis")
 
-# Modèles de traduction
+# Modèles de traduction (optionnels, désactivés pour optimisation)
 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 désactivée pour l'instant
+# translator_to_fr = pipeline("translation", model="Helsinki-NLP/opus-mt-en-fr")
 
-# Fonction pour traduire un texte long en le segmentant
+# Fonction pour traduire un texte long en le segmentant (désactivée pour l'instant)
 def safe_translate_to_fr(text, max_length=512):
-    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)
+    return "Traduction désactivée pour l'instant pour améliorer la vitesse."
 
 # Fonction pour suggérer le meilleur modèle
 def suggest_model(text):
@@ -60,20 +55,20 @@ def suggest_model(text):
 def create_sentiment_gauge(sentiment, score):
     score_percentage = score * 100
     if sentiment.lower() == "neutral":
-        color = "gray"
+        color = "#A9A9A9"
     elif sentiment.lower() == "positive":
-        color = "green"
+        color = "#2E8B57"
     elif sentiment.lower() == "negative":
-        color = "red"
+        color = "#DC143C"
     else:
-        color = "gray"
+        color = "#A9A9A9"
 
     html = f"""
     <div style='width: 100%; max-width: 300px; margin: 10px 0;'>
-        <div style='background-color: #e0e0e0; border-radius: 5px; height: 20px; position: relative; box-shadow: 0 2px 4px rgba(0,0,0,0.2);'>
+        <div style='background-color: #D3D3D3; border-radius: 5px; height: 20px; position: relative; box-shadow: 0 2px 4px rgba(0,0,0,0.2);'>
             <div style='background-color: {color}; width: {score_percentage}%; height: 100%; border-radius: 5px; transition: width 0.3s ease-in-out;'>
             </div>
-            <span style='position: absolute; top: 0; left: 50%; transform: translateX(-50%); color: black; font-size: 12px; line-height: 20px; font-weight: 600;'>
+            <span style='position: absolute; top: 0; left: 50%; transform: translateX(-50%); color: #0A1D37; font-size: 12px; line-height: 20px; font-weight: 600;'>
                 {score_percentage:.1f}%
             </span>
         </div>
@@ -84,48 +79,51 @@ def create_sentiment_gauge(sentiment, score):
     """
     return html
 
-# Fonction d'analyse
-def full_analysis(text, mode, detail_mode, count, history):
+# Fonction d'analyse corrigée
+async def full_analysis(text, mode, detail_mode, count, history):
     if not text:
         return "Entrez une phrase.", "", "", 0, history, None, ""
 
+    # Message de progression
+    yield "Analyse en cours... (Étape 1 : Détection de la langue)", "", "", count, history, None, ""
+
     try:
         lang = detect(text)
     except:
         lang = "unknown"
 
     if lang != "en":
-        text = translator_to_en(text, max_length=512)[0]['translation_text']
-
-    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}"
+        text_en = translator_to_en(text, max_length=512)[0]['translation_text']
+    else:
+        text_en = text
 
-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, mode)
+    yield "Analyse en cours... (Étape 2 : Analyse du sentiment)", "", "", count, history, None, ""
 
-    result = classifier(prediction_response)[0]
+    # Analyse du sentiment avec RoBERTa sur le texte d'entrée
+    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'])
 
+    yield "Analyse en cours... (Étape 3 : Explication de l'impact)", "", "", count, history, None, ""
+
+    # Appel à Zephyr pour expliquer l'impact basé sur le sentiment
     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 event is: {result['label'].lower()}.
 
-The predicted sentiment for this impact 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. {"Use simple language for a general audience." if detail_mode == "Normal" else "Use detailed financial terminology for an expert audience."}
+Assume the event happens (e.g., if the question is "Will the Federal Reserve raise interest rates?", assume they do raise rates). Explain why this event would likely have a {result['label'].lower()} economic impact. Provide a concise explanation in one paragraph, focusing on the potential effects on the economy. {"Use simple language for a general audience." if detail_mode == "Normal" else "Use detailed financial terminology for an expert audience."}
 </s>
 <|assistant|>"""
-    explanation_en = call_zephyr_api(explanation_prompt, mode)
+    explanation_en = await call_zephyr_api(explanation_prompt, mode)
+
+    yield "Analyse en cours... (Étape 4 : Traduction)", "", "", count, history, None, ""
+
+    # Traduction (désactivée pour l'instant)
     explanation_fr = safe_translate_to_fr(explanation_en, max_length=512)
 
     count += 1
@@ -137,7 +135,7 @@ 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
+    return sentiment_output, explanation_en, explanation_fr, count, history, sentiment_gauge, ""
 
 # Fonction pour télécharger historique CSV
 def download_history(history):
@@ -152,25 +150,39 @@ def download_history(history):
 def launch_app():
     custom_css = """
     @import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;600;700&display=swap');
+    @import url('https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.0/css/all.min.css');
 
     body {
-        background: linear-gradient(135deg, #1A1A2E 0%, #16213E 100%);
+        background-image: url('https://images.unsplash.com/photo-1593672715438-d88a70629abe?q=80&w=2070&auto=format&fit=crop');
+        background-size: cover;
+        background-position: center;
+        background-attachment: fixed;
+        background-color: #0A1D37;
+        background-blend-mode: overlay;
         color: #E0E0E0;
         font-family: 'Inter', sans-serif;
+        margin: 0;
+        padding: 20px;
     }
 
     .gr-box {
-        background: #2A2A4A !important;
+        background: #1A3C34 !important;
         border-radius: 12px !important;
-        border: 1px solid #3A3A5A !important;
-        box-shadow: 0 4px 12px rgba(0, 0, 0, 0.3) !important;
-        padding: 20px !important;
-        margin: 10px 0 !important;
+        border: 1px solid #FFD700 !important;
+        box-shadow: 0 6px 16px rgba(0, 0, 0, 0.4) !important;
+        padding: 25px !important;
+        margin: 15px 0 !important;
+        transition: transform 0.2s ease, box-shadow 0.3s ease !important;
+    }
+
+    .gr-box:hover {
+        transform: translateY(-3px) !important;
+        box-shadow: 0 8px 20px rgba(255, 215, 0, 0.3) !important;
     }
 
     .gr-textbox, .gr-dropdown {
-        background: #3A3A5A !important;
-        border: 1px solid #4A4A7A !important;
+        background: #2E4A43 !important;
+        border: 1px solid #FFD700 !important;
         border-radius: 8px !important;
         color: #E0E0E0 !important;
         font-size: 16px !important;
@@ -180,38 +192,44 @@ def launch_app():
 
     .gr-textbox:focus, .gr-dropdown:focus {
         border-color: #FFD700 !important;
-        box-shadow: 0 0 8px rgba(255, 215, 0, 0.3) !important;
+        box-shadow: 0 0 10px rgba(255, 215, 0, 0.4) !important;
     }
 
     .gr-button {
         background: linear-gradient(90deg, #FFD700 0%, #D4AF37 100%) !important;
-        color: #1A1A2E !important;
+        color: #0A1D37 !important;
         border: none !important;
         border-radius: 8px !important;
         padding: 12px 24px !important;
         font-weight: 600 !important;
         font-size: 16px !important;
         transition: transform 0.1s ease, box-shadow 0.3s ease !important;
-        box-shadow: 0 2px 8px rgba(255, 215, 0, 0.2) !important;
+        box-shadow: 0 3px 10px rgba(255, 215, 0, 0.3) !important;
     }
 
     .gr-button:hover {
         transform: translateY(-2px) !important;
-        box-shadow: 0 4px 12px rgba(255, 215, 0, 0.4) !important;
+        box-shadow: 0 6px 14px rgba(255, 215, 0, 0.5) !important;
     }
 
     h1, h2, h3 {
         color: #FFD700 !important;
         font-weight: 700 !important;
-        text-shadow: 0 2px 4px rgba(0, 0, 0, 0.2) !important;
+        text-shadow: 0 2px 4px rgba(0, 0, 0, 0.3) !important;
+        animation: fadeIn 1s ease-in-out;
+    }
+
+    @keyframes fadeIn {
+        from { opacity: 0; transform: translateY(-10px); }
+        to { opacity: 1; transform: translateY(0); }
     }
 
     .gr-row {
-        margin: 15px 0 !important;
+        margin: 20px 0 !important;
     }
 
     .gr-column {
-        padding: 10px !important;
+        padding: 15px !important;
     }
 
     label {
@@ -224,35 +242,35 @@ def launch_app():
     }
 
     label::before {
-        content: '📊 ';
+        font-family: "Font Awesome 6 Free";
+        font-weight: 900;
         margin-right: 8px;
     }
 
+    .gr-textbox label::before {
+        content: '\\f201';
+    }
+
     .gr-html label::before {
-        content: '📈 ';
+        content: '\\f080';
     }
 
     .gr-file label::before {
-        content: '💾 ';
+        content: '\\f019';
     }
 
     .economic-question-section {
-        background-image: url('https://images.unsplash.com/photo-1611974789855-9c2a0a7236a3?q=80&w=2070&auto=format&fit=crop');
-        background-size: cover;
-        background-position: center;
-        background-repeat: no-repeat;
-        background-color: rgba(42, 42, 74, 0.9);
-        background-blend-mode: overlay;
+        background: rgba(26, 60, 52, 0.9) !important;
         border-radius: 12px;
-        padding: 20px;
+        padding: 25px;
         margin: 20px 0;
-        box-shadow: 0 4px 12px rgba(0, 0, 0, 0.3);
+        box-shadow: 0 6px 16px rgba(0, 0, 0, 0.4);
     }
 
     .economic-question-section .gr-textbox {
-        background: rgba(58, 58, 90, 0.8) !important;
+        background: rgba(46, 74, 67, 0.8) !important;
         border: 2px solid #FFD700 !important;
-        box-shadow: 0 2px 8px rgba(255, 215, 0, 0.2) !important;
+        box-shadow: 0 3px 10px rgba(255, 215, 0, 0.3) !important;
         font-size: 18px !important;
         padding: 15px !important;
     }
@@ -262,43 +280,73 @@ def launch_app():
         box-shadow: 0 0 12px rgba(255, 215, 0, 0.5) !important;
     }
 
+    .prompt-box {
+        background: rgba(46, 74, 67, 0.6) !important;
+        border: 1px solid #FFD700 !important;
+        border-radius: 8px !important;
+        color: #E0E0E0 !important;
+        font-size: 16px !important;
+        padding: 12px !important;
+        margin-top: 10px !important;
+    }
+
+    .prompt-box label::before {
+        content: '\\f075';
+    }
+
     .options-section {
         display: flex;
-        justify-content: center;
-        gap: 20px;
+        flex-direction: column;
+        gap: 15px;
         margin-top: 15px;
     }
 
     .options-section .gr-dropdown {
         width: 200px !important;
     }
+
+    .options-section .gr-dropdown label::before {
+        content: '\\f0c9';
+    }
+
+    .progress-message {
+        color: #FFD700 !important;
+        font-style: italic;
+        margin-bottom: 10px;
+    }
     """
 
     with gr.Blocks(theme=gr.themes.Base(), css=custom_css) as iface:
         gr.Markdown("# 📈 Analyse Financière Premium + Explication IA", elem_id="title")
-        gr.Markdown("Posez une question sur un événement économique. L'IA prédit l'impact et attribue un sentiment (positif, négatif, neutre).", elem_classes=["subtitle"])
+        gr.Markdown("Posez une question sur un événement économique. L'IA analyse le sentiment et explique l'impact.", elem_classes=["subtitle"])
 
         count = gr.State(0)
         history = gr.State([])
 
         with gr.Row(elem_classes=["economic-question-section"]):
-            with gr.Column(scale=1, min_width=600):
-                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(elem_classes=["options-section"]):
-                    mode_selector = gr.Dropdown(
-                        choices=["Rapide", "Équilibré", "Précis"],
-                        value="Équilibré",
-                        label="Mode (longueur et style de réponse)"
+            with gr.Column(scale=2):
+                with gr.Column():
+                    input_text = gr.Textbox(
+                        lines=4,
+                        label="Question Économique"
                     )
-                    detail_mode_selector = gr.Dropdown(
-                        choices=["Normal", "Expert"],
-                        value="Normal",
-                        label="Niveau de détail (simplicité ou technicité)"
+                    prompt_display = gr.Textbox(
+                        value="Une hausse des taux causerait-elle une récession ?",
+                        label="Exemple de Prompt",
+                        interactive=False,
+                        elem_classes=["prompt-box"]
                     )
+            with gr.Column(scale=1, elem_classes=["options-section"]):
+                mode_selector = gr.Dropdown(
+                    choices=["Rapide", "Équilibré", "Précis"],
+                    value="Équilibré",
+                    label="Mode (longueur et style de réponse)"
+                )
+                detail_mode_selector = gr.Dropdown(
+                    choices=["Normal", "Expert"],
+                    value="Normal",
+                    label="Niveau de détail (simplicité ou technicité)"
+                )
 
         with gr.Row():
             analyze_btn = gr.Button("Analyser")
@@ -307,7 +355,8 @@ def launch_app():
 
         with gr.Row():
             with gr.Column(scale=1):
-                sentiment_output = gr.Textbox(label="Résultat du Sentiment Prédictif")
+                progress_message = gr.Textbox(label="Progression", elem_classes=["progress-message"], interactive=False)
+                sentiment_output = gr.Textbox(label="Sentiment Prédictif")
                 sentiment_gauge = gr.HTML(label="Jauge de Sentiment")
             with gr.Column(scale=2):
                 with gr.Row():
@@ -321,7 +370,7 @@ 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, explanation_output_en, explanation_output_fr, count, history, sentiment_gauge, progress_message]
         )
 
         download_btn.click(
@@ -330,7 +379,7 @@ def launch_app():
             outputs=[download_file]
         )
 
-    iface.launch(share=True)  # Ajout de share=True
+    iface.launch(share=True)
 
 if __name__ == "__main__":
     launch_app()