Update app.py

app.py

@@ -1,102 +1,103 @@
-from gradio_client import Client, handle_file
-import pandas as pd
-import gradio as gr
-from vosk import Model, KaldiRecognizer
-import json
-import wave
-
-clientEngText = Client("dj-dawgs-ipd/IPD-Text-English-Finetune")
-clientHingText = Client("dj-dawgs-ipd/IPD-Text-Hinglish")
-clientAud = Client("dj-dawgs-ipd/IPD_Audio_HuBERT")
-
-profanity_df = pd.read_csv('Hinglish_Profanity_List.csv', encoding='utf-8')
-profanity_hn = profanity_df['profanity_hn']
-vosk_model = Model(lang="en-us")
-
-
-# import whisper
-# def stt_whisper(file_path):
-#     model = whisper.load_model("base")
-#     try:
-#         result = model.transcribe(file_path)
-#         return result["text"]
-#     except Exception as e:
-#           print(e)
-#         return ""
-
-
-def stt_vosk(file_path):
-    try:
-        wf = wave.open(file_path, "rb")
-        rec = KaldiRecognizer(vosk_model, wf.getframerate())
-        rec.SetWords(True)
-        rec.SetPartialWords(True)
-        while True:
-            data = wf.readframes(4000)
-            if len(data) == 0:
-                break
-            rec.AcceptWaveform(data)
-        data = json.loads(rec.FinalResult())
-        return data["text"]
-    except:
-        return ""
-
-
-def extract_text(audio_path):
-    return stt_vosk(audio_path).lower()
-
-
-def predict_hate_speech(audio_path):
-
-    audResult = clientAud.predict(
-        audio_path=handle_file(audio_path),
-        api_name="/predict"
-    )
-    audResult = json.loads(audResult.replace("'", '"'))
-
-    stt_text = extract_text(audio_path)
-
-    engResult = clientEngText.predict(
-        text=stt_text[:200],
-        api_name="/predict"
-    )
-
-    hingResult = clientHingText.predict(
-        text=stt_text[:200],
-        api_name="/predict"
-    )
-
-    profanityFound = any(word in stt_text.split() for word in profanity_hn)
-    threshold = 0.6
-    isHate = (engResult[0] != "NEITHER" and engResult[1] > threshold) or (
-        hingResult[0] != "NAG" and hingResult[1] > threshold) or (
-        audResult['Classification'] == 'Hate Speech\n' and audResult['Confidence'] > threshold)
-
-    engConf = engResult[1] if engResult[0] != "NEITHER" else (1 - engResult[1])
-    hingConf = hingResult[1] if hingResult[0] != "NEITHER" else (1 - hingResult[1])
-    audConf = audResult['Confidence'] if audResult['Classification'] == 'Hate Speech\n' else (1 - audResult['Confidence'])
-
-    confidence = (engConf + hingConf + audConf) / 3
-    # print(profanityFound, engResult, hingResult, audResult)
-    if profanityFound:
-        return ["hate", f"Result: Profanity Found", f"Text: {stt_text}"]
-    elif isHate:
-        return ["hate", f"Confidence: {confidence}", f"Text: {stt_text}"]
-
-    return ["not_hate", "No hate found, yay!"]
-
-
-iface = gr.Interface(
-    fn=predict_hate_speech,
-    inputs=gr.Audio(type="filepath", label="Upload Audio"),
-    outputs=gr.Textbox(label="Hate Speech Analysis"),
-    title="Hate Speech Audio Pipeline",
-    description="Upload an audio file to detect potential hate speech content.",
-    examples=[
-        ["hate_video_3_3_snippet2.wav"]
-    ],
-    allow_flagging="manual"
-)
-
-if __name__ == "__main__":
-    iface.launch()
+from gradio_client import Client, handle_file
+import pandas as pd
+import gradio as gr
+from vosk import Model, KaldiRecognizer
+import json
+import wave
+
+clientEngText = Client("dj-dawgs-ipd/IPD-Text-English-Finetune")
+clientHingText = Client("dj-dawgs-ipd/IPD-Text-Hinglish")
+clientAud = Client("dj-dawgs-ipd/IPD_Audio_HuBERT")
+
+profanity_df = pd.read_csv('Hinglish_Profanity_List.csv', encoding='utf-8')
+profanity_hn = profanity_df['profanity_hn']
+vosk_model = Model(lang="en-us")
+
+
+# import whisper
+# def stt_whisper(file_path):
+#     model = whisper.load_model("base")
+#     try:
+#         result = model.transcribe(file_path)
+#         return result["text"]
+#     except Exception as e:
+#           print(e)
+#         return ""
+
+
+def stt_vosk(file_path):
+    try:
+        wf = wave.open(file_path, "rb")
+        rec = KaldiRecognizer(vosk_model, wf.getframerate())
+        rec.SetWords(True)
+        rec.SetPartialWords(True)
+        while True:
+            data = wf.readframes(4000)
+            if len(data) == 0:
+                break
+            rec.AcceptWaveform(data)
+        data = json.loads(rec.FinalResult())
+        return data["text"]
+    except:
+        return ""
+
+
+def extract_text(audio_path):
+    return stt_vosk(audio_path).lower()
+
+
+def predict_hate_speech(audio_path):
+
+    audResult = clientAud.predict(
+        audio_path=handle_file(audio_path),
+        api_name="/predict"
+    )
+    audResult = json.loads(audResult.replace("'", '"'))
+
+    stt_text = extract_text(audio_path)
+
+    engResult = clientEngText.predict(
+        text=stt_text[:200],
+        api_name="/predict"
+    )
+
+    hingResult = clientHingText.predict(
+        text=stt_text[:200],
+        api_name="/predict"
+    )
+
+    profanityFound = any(word in stt_text.split() for word in profanity_hn)
+    threshold = 0.6
+    isHate = (engResult[0] != "NEITHER" and engResult[1] > threshold) or (
+        hingResult[0] != "NAG" and hingResult[1] > threshold) or (
+        audResult['Classification'] == 'Hate Speech\n' and audResult['Confidence'] > threshold)
+
+    engConf = engResult[1] if engResult[0] != "NEITHER" else (1 - engResult[1])
+    hingConf = hingResult[1] if hingResult[0] != "NEITHER" else (1 - hingResult[1])
+    audConf = audResult['Confidence'] if audResult['Classification'] == 'Hate Speech\n' else (1 - audResult['Confidence'])
+
+    confidence = (engConf + hingConf + audConf) / 3
+    # print(profanityFound, engResult, hingResult, audResult)
+    if profanityFound:
+        return ["hate", f"Result: Profanity Found", f"Text: {stt_text}"]
+    elif isHate:
+        return ["hate", f"Confidence: {confidence}", f"Text: {stt_text}"]
+
+    return ["not_hate", "No hate found, yay!"]
+
+
+iface = gr.Interface(
+    fn=predict_hate_speech,
+    inputs=gr.Audio(type="filepath", label="Upload Audio"),
+    outputs=gr.Textbox(label="Hate Speech Analysis"),
+    title="Hate Speech Audio Pipeline",
+    description="Upload an audio file to detect potential hate speech content.",
+    examples=[
+        ["hate_1.wav"],
+        ["hate_2.wav"]
+    ],
+    allow_flagging="manual"
+)
+
+if __name__ == "__main__":
+    iface.launch()