Update app.py

app.py

@@ -1,21 +1,38 @@
 import gradio as gr
 from transformers import pipeline
+import torch
 
-# Load sentiment analysis model
-sentiment = pipeline("sentiment-analysis")
+# Load Whisper model and tokenizer
+whisper_model = "openai/whisper-base"
+device = "cuda" if torch.cuda.is_available() else "cpu"
+asr = pipeline("automatic-speech-recognition", model=whisper_model, device=device)
 
-# Function to analyze sentiment
-def get_sentiment(input_text):
-    return sentiment(input_text)[0]['label']
+# Load Sentiment Analysis model
+sentiment_model = "cardiffnlp/twitter-roberta-base-sentiment-latest"
+sentiment_analyzer = pipeline("sentiment-analysis", model=sentiment_model, device=device)
 
-# Create Gradio interface
-iface = gr.Interface(
-    fn=get_sentiment,
-    inputs="text",
-    outputs="text",
-    title="Sentiment Analysis",
-    description="Get Sentiment (Negative/Positive) for the given input"
+def transcribe_and_analyze(audio):
+    # Transcribe audio to text
+    transcription = asr(audio)["text"]
+    
+    # Analyze sentiment of the transcription
+    sentiment = sentiment_analyzer(transcription)[0]
+    
+    return transcription, sentiment["label"], sentiment["score"]
+
+# Define Gradio interface
+interface = gr.Interface(
+    fn=transcribe_and_analyze,
+    inputs=gr.Audio(sources=["microphone", "upload"], type="filepath", label="Upload or Record Audio"),  # Added upload option
+    outputs=[
+        gr.Textbox(label="Transcription"),
+        gr.Textbox(label="Sentiment"),
+        gr.Number(label="Confidence Score")
+    ],
+    title="Real-Time Audio Transcription and Sentiment Analysis - Sumit",
+    description="This application transcribes audio input and analyzes the sentiment of the transcribed text.",
+    live=True
 )
 
-# Launch the app
-iface.launch(share=True)
+if __name__ == "__main__":
+    interface.launch()