Update app.py

app.py

@@ -6,18 +6,15 @@ import uuid
 import csv
 import os
 
-# Load Whisper model
+# Load models
 whisper_model = whisper.load_model("base")
-
-# Load FinBERT model
-finbert_model = "yiyanghkust/finbert-tone"
-tokenizer = AutoTokenizer.from_pretrained(finbert_model)
-model = AutoModelForSequenceClassification.from_pretrained(finbert_model)
+tokenizer = AutoTokenizer.from_pretrained("yiyanghkust/finbert-tone")
+model = AutoModelForSequenceClassification.from_pretrained("yiyanghkust/finbert-tone")
 labels = ["Positive", "Negative", "Neutral"]
 
-# Main function
-def analyze_sentiment(audio_path, client_type):
-    transcription_result = whisper_model.transcribe(audio_path)
+# Analysis Function
+def analyze_sentiment_auto(audio_path, client_type):
+    transcription_result = whisper_model.transcribe(audio_path, task="translate")
     transcript = transcription_result["text"].strip()
 
     inputs = tokenizer(transcript, return_tensors="pt", truncation=True)
@@ -43,18 +40,67 @@ def analyze_sentiment(audio_path, client_type):
             writer.writeheader()
         writer.writerow(result)
 
-    return result
-
-# Gradio Interface
-interface = gr.Interface(
-    fn=analyze_sentiment,
-    inputs=[
-        gr.Audio(sources=["upload"], type="filepath", label="Upload Voice File"),
-        gr.Dropdown(choices=["New", "Renewal", "Support"], label="Client Type")
-    ],
-    outputs="json",
-    title="📊 Voice Sentiment Analyzer",
-    description="Upload a voice note. Select client type and analyze sentiment using FinBERT."
-)
-
-interface.launch()
+    return (
+        transcript,
+        f"🆔 {client_id}",
+        f"📋 {client_type}",
+        f"{sentiment}"
+    )
+
+# Color map for sentiment
+def sentiment_color(sentiment):
+    return {
+        "Positive": "#22c55e",  # Green
+        "Negative": "#ef4444",  # Red
+        "Neutral": "#facc15"    # Yellow
+    }.get(sentiment, "#e5e7eb")
+
+# Gradio UI
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("""
+    <h1 style='text-align: center; color: #1d4ed8;'>🎙️ Multilingual Voice Sentiment Analyzer</h1>
+    <p style='text-align: center; font-size: 16px;'>
+        Upload a voice note in any language. It will be auto-translated to English and analyzed using FinBERT.
+    </p>
+    """)
+
+    with gr.Row():
+        audio_input = gr.Audio(sources=["upload"], type="filepath", label="🔊 Upload Voice File")
+        client_type = gr.Dropdown(choices=["New", "Renewal", "Support"], value="New", label="👤 Client Type")
+
+    submit_btn = gr.Button("🚀 Analyze Sentiment", variant="primary")
+
+    with gr.Row():
+        transcript_output = gr.Textbox(label="📝 English Transcript", lines=4, interactive=False)
+        client_id_output = gr.Textbox(label="🆔 Client ID", interactive=False)
+        client_type_output = gr.Textbox(label="👤 Client Type", interactive=False)
+        sentiment_output = gr.Textbox(label="📊 Sentiment", interactive=False)
+
+    def process(audio, client_type):
+        transcript, client_id, ctype, sentiment = analyze_sentiment_auto(audio, client_type)
+        return (
+            gr.update(value=transcript),
+            gr.update(value=client_id),
+            gr.update(value=ctype),
+            gr.update(value=sentiment, label=f"📊 Sentiment: {sentiment}", elem_id="sentiment-output")
+        )
+
+    submit_btn.click(
+        fn=process,
+        inputs=[audio_input, client_type],
+        outputs=[transcript_output, client_id_output, client_type_output, sentiment_output]
+    )
+
+    gr.Markdown("""
+    <style>
+    #sentiment-output textarea {
+        font-weight: bold;
+        text-align: center;
+        font-size: 1.2em;
+        color: white;
+        background-color: #1f2937;
+    }
+    </style>
+    """)
+
+demo.launch()