Update app.py

app.py

@@ -1,29 +1,21 @@
 import gradio as gr
-import requests
 from transformers import pipeline
 from langdetect import detect
+from huggingface_hub import InferenceClient
 import pandas as pd
-import textstat
-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}}
-    
+    client = InferenceClient("HuggingFaceH4/zephyr-7b-beta", token=hf_token)
     try:
-        response = requests.post(API_URL, headers=headers, json=payload, timeout=60)
-        response.raise_for_status()
-        return response.json()[0]["generated_text"]
+        response = client.text_generation(prompt, max_new_tokens=300)
+        return response
     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")
 
@@ -41,25 +33,10 @@ def suggest_model(text):
     else:
         return "Précis"
 
-# Fonction pour générer un graphique de clarté
-def plot_clarity(clarity_scores):
-    plt.figure(figsize=(8, 4))
-    plt.plot(range(1, len(clarity_scores) + 1), clarity_scores, marker='o')
-    plt.title("Évolution du Score de Clarté")
-    plt.xlabel("Numéro d'analyse")
-    plt.ylabel("Score de Clarté")
-    plt.ylim(0, 100)
-    plt.grid(True)
-    return plt.gcf()
-
-# Fonction pour reset le graphique
-def reset_clarity_graph():
-    return [], plot_clarity([])
-
 # Fonction d'analyse
-def full_analysis(text, mode, detail_mode, count, history, clarity_scores):
+def full_analysis(text, mode, detail_mode, count, history):
     if not text:
-        return "Entrez une phrase.", "", "", 0, history, clarity_scores, None, None
+        return "Entrez une phrase.", "", "", 0, history, None
 
     try:
         lang = detect(text)
@@ -72,30 +49,35 @@ def full_analysis(text, mode, detail_mode, count, history, clarity_scores):
     result = classifier(text)[0]
     sentiment_output = f"Sentiment : {result['label']} (Score: {result['score']:.2f})"
 
-    prompt = f"""
-    You are a financial analyst AI.
-    Based on the following financial news: \"{text}\",
-    explain clearly why the sentiment is {result['label'].lower()}.
-    {"Write a concise paragraph." if detail_mode == "Normal" else "Write a detailed explanation over multiple paragraphs."}
-    """
-
+    prompt = f"""<|system|>
+You are a professional financial analyst AI.
+</s>
+<|user|>
+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.
+</s>
+<|assistant|>"""
     explanation_en = call_zephyr_api(prompt)
     explanation_fr = translator_to_fr(explanation_en, max_length=512)[0]['translation_text']
 
-    clarity_score = textstat.flesch_reading_ease(explanation_en)
-    clarity_scores.append(clarity_score)
-
     count += 1
     history.append({
         "Texte": text,
         "Sentiment": result['label'],
         "Score": f"{result['score']:.2f}",
         "Explication_EN": explanation_en,
-        "Explication_FR": explanation_fr,
-        "Clarté": f"{clarity_score:.1f}"
+        "Explication_FR": explanation_fr
     })
 
-    return sentiment_output, explanation_en, explanation_fr, clarity_score, count, history, clarity_scores, plot_clarity(clarity_scores)
+    return sentiment_output, explanation_en, explanation_fr, count, history
 
 # Fonction pour télécharger historique CSV
 def download_history(history):
@@ -109,13 +91,11 @@ def download_history(history):
 # 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([])
-        clarity_scores = gr.State([])
 
         with gr.Row():
             input_text = gr.Textbox(lines=4, placeholder="Entrez une actualité ici...", label="Texte à analyser")
@@ -145,21 +125,14 @@ def launch_app():
             with gr.Column():
                 explanation_output_fr = gr.Textbox(label="Explication en Français")
 
-        clarity_score_output = gr.Textbox(label="Score de Clarté (Flesch Reading Ease)")
-        clarity_plot = gr.Plot(label="Graphique des Scores de Clarté")
         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, clarity_scores],
-            outputs=[sentiment_output, explanation_output_en, explanation_output_fr, clarity_score_output, count, history, clarity_scores, clarity_plot]
-        )
-
-        reset_graph_btn.click(
-            reset_clarity_graph,
-            outputs=[clarity_scores, clarity_plot]
+            inputs=[input_text, mode_selector, detail_mode_selector, count, history],
+            outputs=[sentiment_output, explanation_output_en, explanation_output_fr, count, history]
         )
 
         download_btn.click(