Update app.py

app.py

@@ -1,5 +1,6 @@
 import streamlit as st
 import requests
+import json
 
 # Define API details
 API_URL = "https://api-inference.huggingface.co/models/ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition"
@@ -9,13 +10,22 @@ headers = {"Authorization": "Bearer hf_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"}
 def query_api(audio_file):
     with open(audio_file, "rb") as f:
         response = requests.post(API_URL, headers=headers, data=f)
-    return response.json()
+    try:
+        response_json = response.json()
+    except json.JSONDecodeError:
+        st.error("Error decoding JSON from API response.")
+        response_json = {}
+    return response_json
 
 # Function to compute the neg_rate from API response
 def compute_neg_rate(emotion_data):
-    neg_emotions = ["anger", "disgust", "fear", "sadness"]  # Example negative emotions
-    neg_rate = sum([emotion['score'] for emotion in emotion_data if emotion['label'].lower() in neg_emotions])
-    return neg_rate * 100  # Scale to a percentage
+    if isinstance(emotion_data, list):  # Ensure the data is a list
+        neg_emotions = ["anger", "disgust", "fear", "sadness"]  # Example negative emotions
+        neg_rate = sum([emotion['score'] for emotion in emotion_data if emotion['label'].lower() in neg_emotions])
+        return neg_rate * 100  # Scale to a percentage
+    else:
+        st.error("Unexpected API response format.")
+        return 0
 
 # Depression scale logic
 def analyze_depression(neg_rate):
@@ -76,9 +86,8 @@ def main():
 
             # Query API and compute neg_rate
             emotion_data = query_api("temp_audio.wav")
+            st.write("API Response:", emotion_data)  # Debugging line to inspect response
             neg_rate = compute_neg_rate(emotion_data)
-
-            # Analyze and display results
             result = analyze_depression(neg_rate)
             st.text(result)