Update app.py

app.py

@@ -1,23 +1,18 @@
 import gradio as gr
-import requests
 from transformers import pipeline
 from langdetect import detect
+from huggingface_hub import InferenceClient
 import pandas as pd
-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}}
-
+def call_zephyr_api(messages, hf_token=HF_TOKEN):
+    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.chat_completion(messages, max_tokens=300)
+        return response.choices[0].message.content
     except Exception as e:
         raise gr.Error(f"❌ Erreur d'appel API Hugging Face : {str(e)}")
 
@@ -54,24 +49,13 @@ def full_analysis(text, mode, detail_mode, count, history):
     result = classifier(text)[0]
     sentiment_output = f"Sentiment : {result['label']} (Score: {result['score']:.2f})"
 
-    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()}.
+    messages = [
+        {"role": "system", "content": "You are a professional financial analyst AI."},
+        {"role": "user", "content": f"Analyze the following financial news carefully:\n\"{text}\"\n\nThe detected sentiment for this news is: {result['label'].lower()}.\n\nNow, explain why the sentiment is {result['label'].lower()} using a logical, fact-based explanation.\nBase your reasoning only on the given news text.\nDo not repeat the news text or the prompt.\nRespond only with your financial analysis in one clear paragraph.\nWrite in a clear and professional tone."}
+    ]
+    explanation_en = call_zephyr_api(messages)
 
-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_en = call_zephyr_api(messages)
     explanation_fr = translator_to_fr(explanation_en, max_length=512)[0]['translation_text']
 
     count += 1
@@ -97,7 +81,6 @@ 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.")