Update app.py

app.py

@@ -3,38 +3,34 @@ import requests
 import pandas as pd
 import textstat
 import os
-from transformers import pipeline
 
 # Récupération du token Hugging Face
 HF_TOKEN = os.getenv("HF_TOKEN")
 
-# Fonction pour appeler l'API Zephyr-7B-Beta
+# Fonction pour appeler l'API Zephyr-7B
 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}}
+    payload = {"inputs": prompt}
 
     try:
         response = requests.post(API_URL, headers=headers, json=payload, timeout=60)
         response.raise_for_status()
         return response.json()[0]["generated_text"].strip()
     except Exception as e:
-        raise gr.Error(f"Erreur d'appel API Hugging Face : {str(e)}")
-
-# Pipeline d'analyse de sentiment initial (optionnel)
-classifier = pipeline("sentiment-analysis", model="mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis")
+        raise gr.Error(f"❌ Erreur d'appel API Hugging Face : {str(e)}")
 
 # Fonction principale d'analyse
 def full_analysis(text, history):
     if not text:
-        return "Entrez une phrase.", "", 0, history, None
+        return "Entrez une phrase.", "", 0, history, 0
 
-    # 1. Demander à Zephyr de donner uniquement le sentiment
+    # 1. Demander à Zephyr de détecter le sentiment
     prompt_sentiment = f"""
 You are a financial news sentiment detector.
 
 Given the following news text:
-"{text}"
+\"{text}\"
 
 Respond only with one word: positive, neutral, or negative.
 
@@ -45,12 +41,12 @@ Do not add any explanation or extra text.
     if detected_sentiment not in ["positive", "neutral", "negative"]:
         detected_sentiment = "neutral"
 
-    # 2. Demander l'explication basée sur ce sentiment
+    # 2. Demander à Zephyr d'expliquer
     prompt_explanation = f"""
 You are a financial analyst AI.
 
 Given the following financial news:
-"{text}"
+\"{text}\"
 
 The detected sentiment is: {detected_sentiment}.
 
@@ -59,11 +55,11 @@ Write a concise paragraph.
 """
     explanation = call_zephyr_api(prompt_explanation)
 
-    # 3. Calculer le score de clarté
+    # 3. Calculer la clarté
     clarity_score = textstat.flesch_reading_ease(explanation)
-    clarity_score = max(0, min(clarity_score, 100))  # Bornage 0-100
+    clarity_score = max(0, min(clarity_score, 100))  # Limité entre 0-100
 
-    # 4. Stocker dans l'historique
+    # 4. Sauvegarder dans l'historique
     history.append({
         "Texte": text,
         "Sentiment": detected_sentiment.capitalize(),
@@ -71,20 +67,7 @@ Write a concise paragraph.
         "Explication": explanation
     })
 
-    return detected_sentiment.capitalize(), explanation, clarity_score, history, clarity_score
-
-# Fonction pour générer la barre de clarté
-def generate_clarity_bar(score, sentiment):
-    color = "green" if sentiment.lower() == "positive" else ("red" if sentiment.lower() == "negative" else "gray")
-    return gr.BarPlot.update(
-        value=[["Clarity", score]],
-        x="label",
-        y="value",
-        colors=[color],
-        width=400,
-        height=50,
-        y_lim=[0, 100]
-    )
+    return detected_sentiment.capitalize(), explanation, clarity_score, history, int(clarity_score)
 
 # Fonction pour télécharger l'historique
 def download_history(history):
@@ -107,12 +90,10 @@ def launch_app():
             analyze_btn = gr.Button("Analyser")
             download_btn = gr.Button("Télécharger l'historique")
 
-        with gr.Row():
-            sentiment_output = gr.Textbox(label="Sentiment Détecté")
-            clarity_bar = gr.BarPlot()
-
+        sentiment_output = gr.Textbox(label="Sentiment Détecté")
         explanation_output = gr.Textbox(label="Explication de l'IA", lines=5)
         clarity_score_text = gr.Textbox(label="Score de Clarté (%)")
+        clarity_slider = gr.Slider(0, 100, label="Clarté (%)", interactive=False)
         file_output = gr.File(label="Fichier CSV")
 
         history = gr.State([])
@@ -120,7 +101,7 @@ def launch_app():
         analyze_btn.click(
             full_analysis,
             inputs=[input_text, history],
-            outputs=[sentiment_output, explanation_output, clarity_score_text, history, clarity_bar]
+            outputs=[sentiment_output, explanation_output, clarity_score_text, history, clarity_slider]
         )
 
         download_btn.click(