Final 1.2

app.py

@@ -1,6 +1,7 @@
 import gradio as gr
 from transformers import pipeline
 from gradio_client import Client, file
+import json
 
 language_classifier = Client("adrien-alloreview/speechbrain-lang-id-voxlingua107-ecapa")
 transcriber = Client("tensorlake/audio-extractors")
@@ -21,13 +22,17 @@ toxic_detector = pipeline(
 
 
 def detect_language(file_path):
-    result = language_classifier.predict(param_0=file(file_path), api_name="/predict")
-    language_result = result["label"].split(": ")[1]
-    if language_result.lower() in ["russian", "belarussian", "ukrainian"]:
-        selected_language = "russian"
-    else:
-        selected_language = "kazakh"
-    return selected_language
+    try:
+        result = language_classifier.predict(param_0=file(file_path), api_name="/predict")
+        language_result = result["label"].split(": ")[1]
+        if language_result.lower() in ["russian", "belarussian", "ukrainian"]:
+            selected_language = "russian"
+        else:
+            selected_language = "kazakh"
+        return selected_language
+    except Exception as e:
+        print(f"Language detection failed: {e}")
+        return None
 
 
 def request_gradio(file_path, language):
@@ -47,31 +52,40 @@ def request_gradio(file_path, language):
         )
         return result
     except Exception as e:
+        print(f"Transcription failed: {e}")
         return None
 
 
 def detect_emotion(audio):
-    res = emotion_detector(audio)
-    emotion_with_max_score = res[0]["label"]
-    return emotion_with_max_score
+    try:
+        res = emotion_detector(audio)
+        emotion_with_max_score = res[0]["label"]
+        return emotion_with_max_score
+    except Exception as e:
+        print(f"Emotion detection failed: {e}")
+        return None
 
 
 def detect_toxic_local(text_whisper):
-    res = toxic_detector([text_whisper])[0]["label"]
-    if res == "toxic":
-        return True
-    if res == "neutral":
-        return False
-    else:
+    try:
+        res = toxic_detector([text_whisper])[0]["label"]
+        if res == "toxic":
+            return True
+        elif res == "neutral":
+            return False
+        else:
+            return None
+    except Exception as e:
+        print(f"Toxicity detection failed: {e}")
         return None
 
 
 def assessment(file_path):
-    language = detect_language(file_path)
-    result_text = request_gradio(file_path, language)
-    result_emotion = detect_emotion(result_text)
-    result_toxic = detect_toxic_local(result_text)
-    return {"emotion": result_emotion, "toxic": result_toxic}
+    language = detect_language(file_path) or "unknown"
+    result_text = request_gradio(file_path, language) or ""
+    result_emotion = detect_emotion(result_text) or "unknown"
+    result_toxic = detect_toxic_local(result_text) or False
+    return json.dumps({"emotion": result_emotion, "toxic": result_toxic})
 
 
 gradio_app = gr.Interface(
@@ -80,4 +94,3 @@ gradio_app = gr.Interface(
     outputs="json"
 )
 gradio_app.launch()
-