Update app.py

app.py

@@ -8,11 +8,11 @@ import os
 HF_TOKEN = os.getenv("HF_TOKEN")
 
 # Fonction pour appeler l'API Zephyr
-def call_zephyr_api(messages, hf_token=HF_TOKEN):
+def call_zephyr_api(prompt, hf_token=HF_TOKEN):
     client = InferenceClient("HuggingFaceH4/zephyr-7b-beta", token=hf_token)
     try:
-        response = client.chat_completion(messages, max_tokens=300)
-        return response.choices[0].message.content
+        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)}")
 
@@ -49,13 +49,23 @@ def full_analysis(text, mode, detail_mode, count, history):
     result = classifier(text)[0]
     sentiment_output = f"Sentiment : {result['label']} (Score: {result['score']:.2f})"
 
-    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)
-
-    explanation_en = call_zephyr_api(messages)
+    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']
 
     count += 1