Update app.py

app.py

@@ -1,22 +1,31 @@
 import gradio as gr
+import requests
 from transformers import pipeline
 from langdetect import detect
 import pandas as pd
 import textstat
+import matplotlib.pyplot as plt
+import os
+
+HF_TOKEN = os.getenv("HF_TOKEN")
+
+
+# Fonction pour appeler l'API Mistral-7B
+def call_mistral_api(prompt, hf_token=HF_TOKEN):
+    API_URL = "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.2"
+    headers = {"Authorization": f"Bearer {hf_token}"}
+    payload = {"inputs": prompt, "parameters": {"max_new_tokens": 300}}
+    response = requests.post(API_URL, headers=headers, json=payload)
+    response.raise_for_status()
+    return response.json()[0]["generated_text"]
 
 # Chargement du modèle de sentiment
-classifier = pipeline(
-    "sentiment-analysis",
-    model="mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis"
-)
+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")
 
-# Modèle explicatif CPU-friendly
-explainer = pipeline("text2text-generation", model="facebook/blenderbot-1B-distill")
-
 # Fonction pour suggérer le meilleur modèle
 def suggest_model(text):
     word_count = len(text.split())
@@ -27,10 +36,25 @@ 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):
+def full_analysis(text, mode, detail_mode, count, history, clarity_scores):
     if not text:
-        return "Entrez une phrase.", "", "", 0, history, None
+        return "Entrez une phrase.", "", "", 0, history, clarity_scores, None, None
 
     try:
         lang = detect(text)
@@ -50,10 +74,11 @@ def full_analysis(text, mode, detail_mode, count, history):
     {"Write a concise paragraph." if detail_mode == "Normal" else "Write a detailed explanation over multiple paragraphs."}
     """
 
-    explanation_en = explainer(prompt, max_length=300 if detail_mode == "Expert" else 150)[0]['generated_text']
+    explanation_en = call_mistral_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({
@@ -65,7 +90,7 @@ def full_analysis(text, mode, detail_mode, count, history):
         "Clarté": f"{clarity_score:.1f}"
     })
 
-    return sentiment_output, explanation_en, explanation_fr, clarity_score, count, history, None
+    return sentiment_output, explanation_en, explanation_fr, clarity_score, count, history, clarity_scores, plot_clarity(clarity_scores)
 
 # Fonction pour télécharger historique CSV
 def download_history(history):
@@ -85,6 +110,7 @@ def launch_app():
 
         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")
@@ -102,6 +128,7 @@ def launch_app():
             )
 
         analyze_btn = gr.Button("Analyser")
+        reset_graph_btn = gr.Button("Reset Graphique")
         download_btn = gr.Button("Télécharger CSV")
 
         with gr.Row():
@@ -114,14 +141,20 @@ def launch_app():
                 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],
-            outputs=[sentiment_output, explanation_output_en, explanation_output_fr, clarity_score_output, count, history, download_file]
+            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]
         )
 
         download_btn.click(