Update app.py

app.py

@@ -3,7 +3,7 @@ from transformers import pipeline
 
 # Lade Modelle von Hugging Face
 llama_pipeline = pipeline("text-generation", model="meta-llama/Meta-Llama-3-8B")
-finbert_pipeline = pipeline("text-classification", model="ProsusAI/finbert")
+#finbert_pipeline = pipeline("text-classification", model="ProsusAI/finbert")
 
 # Funktion für die Analyse
 def analyze_financial_text(model_choice, text_input):
@@ -14,9 +14,9 @@ def analyze_financial_text(model_choice, text_input):
         response = llama_pipeline(text_input, max_length=200, do_sample=True)
         return response[0]["generated_text"]
     
-    elif model_choice == "FinBERT (Sentiment-Analyse)":
-        response = finbert_pipeline(text_input)
-        return f"Ergebnis: {response[0]['label']} (Score: {response[0]['score']:.2f})"
+    #elif model_choice == "FinBERT (Sentiment-Analyse)":
+        #response = finbert_pipeline(text_input)
+        #return f"Ergebnis: {response[0]['label']} (Score: {response[0]['score']:.2f})"
     
     return "Ungültige Auswahl."
 
@@ -25,7 +25,8 @@ with gr.Blocks() as demo:
     gr.Markdown("# Finanz-Analyse mit Sprachmodellen (LLaMA 3 & FinBERT)")
 
     model_choice = gr.Radio(
-        choices=["LLaMA 3 (Text-Generierung)", "FinBERT (Sentiment-Analyse)"],
+        #choices=["LLaMA 3 (Text-Generierung)", "FinBERT (Sentiment-Analyse)"],
+        choices=["LLaMA 3 (Text-Generierung)"],
         label="Wähle ein Modell",
         value="LLaMA 3 (Text-Generierung)"
     )