Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,29 +1,25 @@
-import os
-os.environ["TRANSFORMERS_CACHE"] = "/app/.cache/huggingface"
-os.environ["HF_HOME"] = "/app/.cache/huggingface"
-os.environ["XDG_CACHE_HOME"] = "/app/.cache"
-os.environ["XDG_CONFIG_HOME"] = "/app/.streamlit"
-
 import torch
 import torchaudio
 from transformers import WhisperProcessor, WhisperForConditionalGeneration
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import streamlit as st
+import os
+
+os.environ["TRANSFORMERS_CACHE"] = "/app/.cache"
+os.environ["HF_HOME"] = "/app/.cache"
+
 
-@st.cache_resource
 def load_models():
     whisper_processor = WhisperProcessor.from_pretrained("openai/whisper-tiny")
     whisper_model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny")
-    text_model = AutoModelForSequenceClassification.from_pretrained("Hate-speech-CNERG/dehatebert-mono-english")
-    tokenizer = AutoTokenizer.from_pretrained("Hate-speech-CNERG/dehatebert-mono-english")
+    text_model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased")
+    tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
     return whisper_processor, whisper_model, text_model, tokenizer
 
 whisper_processor, whisper_model, text_model, tokenizer = load_models()
 
 def transcribe(audio_path):
     waveform, sample_rate = torchaudio.load(audio_path)
-    if waveform.shape[0] > 1:
-        waveform = waveform.mean(dim=0, keepdim=True)
     input_features = whisper_processor(waveform.squeeze().numpy(), sampling_rate=sample_rate, return_tensors="pt").input_features
     predicted_ids = whisper_model.generate(input_features)
     transcription = whisper_processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
@@ -32,34 +28,26 @@ def transcribe(audio_path):
 def extract_text_features(text):
     inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
     outputs = text_model(**inputs)
-    prediction = outputs.logits.argmax(dim=1).item()
-    return prediction
-
-def predict_hate_speech(audio_path=None, text=None):
-    if text:
-        text_input = text
-    elif audio_path:
-        transcription = transcribe(audio_path)
-        text_input = transcription
-    else:
-        return "Please provide either audio or text input."
+    return outputs.logits.argmax(dim=1).item()
 
+def predict_hate_speech(audio_path, text):
+    transcription = transcribe(audio_path)
+    text_input = text if text else transcription
     prediction = extract_text_features(text_input)
     return "Hate Speech" if prediction == 1 else "Not Hate Speech"
 
 st.title("Hate Speech Detector with Audio and Text")
 audio_file = st.file_uploader("Upload an audio file (wav, mp3, flac, ogg, opus)", type=["wav", "mp3", "flac", "ogg", "opus"])
 text_input = st.text_input("Optional text input")
-
 if st.button("Predict"):
-    if audio_file is not None:
-        temp_path = "temp_audio.wav"
-        with open(temp_path, "wb") as f:
-            f.write(audio_file.read())
-        prediction = predict_hate_speech(temp_path, text_input)
-        st.success(prediction)
-    elif text_input:
-        prediction = predict_hate_speech(text=text_input)
+    if audio_file is not None or text_input:
+        audio_path = None
+        if audio_file is not None:
+            with open("temp_audio_input", "wb") as f:
+                f.write(audio_file.read())
+            audio_path = "temp_audio_input"
+
+        prediction = predict_hate_speech(audio_path, text_input) if audio_path else extract_text_features(text_input)
         st.success(prediction)
     else:
-        st.warning("Please provide at least audio or text input.")
+        st.warning("Please provide either audio or text input.")