Update app.py

app.py

@@ -1,67 +1,21 @@
 import gradio as gr
 from transformers import pipeline
-import numpy as np
-import librosa
 
-class VoiceIntelligence:
-    def __init__(self):
-        # Initialize lightweight sentiment analyzer
-        try:
-            self.sentiment_analyzer = pipeline(
-                "sentiment-analysis",
-                model="distilbert-base-uncased-finetuned-sst-2-english",
-                max_length=512,
-                truncation=True
-            )
-        except Exception as e:
-            print(f"Error initializing sentiment analyzer: {e}")
-            self.sentiment_analyzer = None
+# Load sentiment analysis model
+sentiment = pipeline("sentiment-analysis")
 
-    def process_audio(self, audio_path):
-        try:
-            if audio_path is None:
-                return "No audio provided", {"error": "No audio input"}
-
-            # Basic audio processing
-            y, sr = librosa.load(audio_path, sr=16000)
-            
-            # Simple voice activity detection
-            intervals = librosa.effects.split(y, top_db=20)
-            if len(intervals) == 0:
-                return "No speech detected", {"error": "No speech detected"}
-
-            # For demo, return simple confirmation
-            text = "Speech detected - Demo Mode"
-            
-            # Analyze sentiment
-            if self.sentiment_analyzer:
-                try:
-                    sentiment = self.sentiment_analyzer(text)
-                    return text, sentiment[0]
-                except Exception as e:
-                    return text, {"label": "ERROR", "score": 0.0}
-            else:
-                return text, {"label": "NEUTRAL", "score": 0.5}
-
-        except Exception as e:
-            return f"Error processing audio: {str(e)}", {"error": str(e)}
-
-# Initialize the system
-system = VoiceIntelligence()
+# Function to analyze sentiment
+def get_sentiment(input_text):
+    return sentiment(input_text)[0]['label']
 
 # Create Gradio interface
-demo = gr.Interface(
-    fn=system.process_audio,
-    inputs=gr.Audio(source="microphone", type="filepath"),
-    outputs=[
-        gr.Textbox(label="Transcription"),
-        gr.JSON(label="Sentiment")
-    ],
-    title="Voice Intelligence Demo",
-    description="Record audio to see transcription and sentiment analysis (Demo Mode)",
-    examples=None,
-    cache_examples=False
+iface = gr.Interface(
+    fn=get_sentiment,
+    inputs="text",
+    outputs="text",
+    title="Sentiment Analysis",
+    description="Get Sentiment (Negative/Positive) for the given input"
 )
 
-if __name__ == "__main__":
-    demo.launch()
+# Launch the app
+iface.launch(share=True)