added sentiment analysis

app.py

@@ -3,6 +3,7 @@ import gradio as gr
 
 pipe_fine = pipeline(model="zeihers-mart/whisper-small-swedish-basic", device_map="auto") 
 pipe_raw = pipeline(model="openai/whisper-small", device_map="auto")
+sa = pipeline('sentiment-analysis', model='marma/bert-base-swedish-cased-sentiment')
 
 # force swedish
 pipe_fine.model.config.forced_decoder_ids = (
@@ -22,13 +23,18 @@ def transcribe(audio):
     print(f"Audio transcribed: {text_sv}")
     text_raw= pipe_raw(audio)["text"]
     print(f"Text translated: {text_raw}")
-    return text_sv, text_raw
+    sentiment= sa(text_sv)["label"]
+    path = "https://upload.wikimedia.org/wikipedia/commons/thumb/e/e0/SNice.svg/1200px-SNice.svg.png"
+    if sentiment == "NEGATIVE":
+        path = "https://upload.wikimedia.org/wikipedia/commons/thumb/4/42/Sad_smiley_yellow_simple.svg/2048px-Sad_smiley_yellow_simple.svg.png"
+    return text_sv, text_raw, path
 
 iface = gr.Interface(
     fn=transcribe, 
     inputs=gr.Audio(sources=["microphone"], type="filepath"), 
     outputs=[gr.Textbox(label="Fine-tuned transcription"),
-             gr.Textbox(label="Whisper Transcription")],
+             gr.Textbox(label="Whisper transcription"),
+             gr.Image(label="Sentiment from Fine-tuned transcription", width=100, height=100)],
     title="Finetuned Whisper Swedish Small",
     description="Realtime demo for Swedish speech recognition using a fine-tuned Whisper small model.",
 )