Update app.py

app.py

@@ -4,6 +4,7 @@ from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassifica
 import torchaudio
 import os
 import jieba
+import magic
 
 # Device setup: automatically selects CUDA or CPU
 device = "cuda" if torch.cuda.is_available() else "cpu"
@@ -31,7 +32,7 @@ def transcribe_audio(audio_path):
         return " ".join(results)
     return pipe(audio_path)["text"]
 
-# Load sentiment analysis model (Custom multilingual sentiment analysis)
+# Load sentiment analysis model
 sentiment_pipe = pipeline("text-classification", model="Leo0129/CustomModel-multilingual-sentiment-analysis", device=device)
 
 # Text splitting function (using jieba for Chinese text)
@@ -91,14 +92,22 @@ def main():
     st.markdown("""
     <div class="header">
         <h1 style='margin:0;'>🎙️ Customer Service Quality Analyzer</h1>
-        <p style='color: white; font-size: 1.2rem;'>Evaluate the service quality with simple-uploading!</p>
+        <p style='color: white; font-size: 1.2rem;'>Evaluate the service quality with simple uploading!</p>
     </div>
     """, unsafe_allow_html=True)
 
+    # Step-by-step instructions
+    st.markdown("📤 **Step 1:** Please upload your Cantonese customer service audio file")
+
     # Audio file uploader
-    uploaded_file = st.file_uploader("👉🏻 Upload your Cantonese audio file here...", type=["wav", "mp3", "flac"])
+    uploaded_file = st.file_uploader("👉🏻 Upload your audio file here...", type=["wav", "mp3", "flac"])
 
     if uploaded_file is not None:
+        file_type = magic.from_buffer(uploaded_file.getbuffer(), mime=True)
+        if not file_type.startswith("audio/"):
+            st.error("⚠️ Sorry, the uploaded file format is not supported. Please upload an audio file in .wav, .mp3, or .flac format.")
+            return
+
         st.audio(uploaded_file, format="audio/wav")
         temp_audio_path = "uploaded_audio.wav"
         with open(temp_audio_path, "wb") as f:
@@ -108,16 +117,24 @@ def main():
         status_container = st.empty()
 
         # Step 1: Audio transcription
-        status_container.info("📝 **Step 1/2**: Transcribing audio...")
-        transcript = transcribe_audio(temp_audio_path)
+        status_container.info("📝 **Step 1:** Transcribing audio, please wait...")
+        with st.spinner('🔄 Transcribing, please wait...'):
+            transcript = transcribe_audio(temp_audio_path)
         progress_bar.progress(50)
         st.write("**Transcript:**", transcript)
 
         # Step 2: Sentiment Analysis
-        status_container.info("🧑‍⚖️ **Step 2/2**: Evaluating sentiment quality...")
+        status_container.info("🧑‍⚖️ **Step 2:** Analyzing sentiment, please wait...")
         quality_rating = rate_quality(transcript)
         progress_bar.progress(100)
-        st.write("**Sentiment Rating:**", quality_rating)
+        st.write("**Sentiment Analysis Result:**", quality_rating)
+
+        # Download analysis results
+        result_text = f"Transcript:\n{transcript}\n\nSentiment Analysis Result: {quality_rating}"
+        st.download_button(label="📥 Download Analysis Report", data=result_text, file_name="analysis_report.txt")
+
+        # Customer support info
+        st.markdown("❓If you encounter any issues, please contact customer support:  📧 **support@hellotoby.com**")
 
         os.remove(temp_audio_path)