Update src/streamlit_app.py

src/streamlit_app.py

@@ -5,22 +5,19 @@ from transformers import WhisperProcessor, WhisperForConditionalGeneration
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import streamlit as st
 
-def load_models():
-    whisper_processor = WhisperProcessor.from_pretrained("openai/whisper-tiny")
-    whisper_model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny")
-    text_model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased")
-    tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
-    return whisper_processor, whisper_model, text_model, tokenizer
+os.environ["TRANSFORMERS_CACHE"] = "/app/.cache/huggingface"
+os.environ["HF_HOME"] = "/app/.cache/huggingface"
+os.environ["TORCH_HOME"] = "/app/.cache/torch"
+os.environ["HF_TOKEN"] = "your_huggingface_access_token"
 
-whisper_processor, whisper_model, text_model, tokenizer = load_models()
+whisper_processor = WhisperProcessor.from_pretrained("openai/whisper-tiny", use_auth_token=True)
+whisper_model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny", use_auth_token=True)
+text_model = AutoModelForSequenceClassification.from_pretrained("GroNLP/hateBERT")
+tokenizer = AutoTokenizer.from_pretrained("GroNLP/hateBERT")
 
 def transcribe(audio_path):
     waveform, sample_rate = torchaudio.load(audio_path)
-    input_features = whisper_processor(
-        waveform.squeeze().numpy(),
-        sampling_rate=sample_rate,
-        return_tensors="pt"
-    ).input_features
+    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]
     return transcription
@@ -34,22 +31,23 @@ def extract_text_features(text):
 def predict(audio_file, text_input):
     if not audio_file and not text_input:
         return "Please provide either an audio file or some text."
-    if audio_file is not None:
+    if audio_file:
         audio_path = "temp_audio.wav"
         with open(audio_path, "wb") as f:
             f.write(audio_file.read())
         transcribed_text = transcribe(audio_path)
         prediction = extract_text_features(text_input or transcribed_text)
-        return f"Predicted: {prediction} \n\n(Transcribed: {transcribed_text})" if not text_input else f"Predicted: {prediction}"
-    elif text_input:
+        if text_input:
+            return f"Predicted: {prediction}"
+        else:
+            return f"Predicted: {prediction} \n\n(Transcribed: {transcribed_text})"
+    else:
         prediction = extract_text_features(text_input)
         return f"Predicted: {prediction}"
 
 st.title("Hate Speech Detector")
-
 uploaded_audio = st.file_uploader("Upload Audio File (.mp3, .wav, .ogg, .flac, .opus)", type=["mp3", "wav", "ogg", "flac", "opus"])
 text_input = st.text_input("Or enter text:")
-
 if st.button("Predict"):
     result = predict(uploaded_audio, text_input)
     st.success(result)