Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,9 +1,6 @@
 import torch
 import os
 import streamlit as st
-from pydub import AudioSegment
-import numpy as np
-from transformers import WhisperProcessor, WhisperForConditionalGeneration
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
 os.environ["TRANSFORMERS_CACHE"] = "/app/.cache/huggingface"
@@ -11,20 +8,9 @@ os.environ["HF_HOME"] = "/app/.cache/huggingface"
 os.environ["TORCH_HOME"] = "/app/.cache/torch"
 hf_token = os.getenv("HateSpeechMujtabatoken")
 
-whisper_processor = WhisperProcessor.from_pretrained("openai/whisper-tiny", token=hf_token)
-whisper_model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny", token=hf_token)
 text_model = AutoModelForSequenceClassification.from_pretrained("GroNLP/hateBERT", token=hf_token)
 tokenizer = AutoTokenizer.from_pretrained("GroNLP/hateBERT", token=hf_token)
 
-def transcribe(audio_path):
-    audio = AudioSegment.from_file(audio_path, format="opus")
-    audio = audio.set_channels(1).set_frame_rate(16000)
-    samples = np.array(audio.get_array_of_samples()).astype(np.float32) / (2**15)
-    input_features = whisper_processor(samples, sampling_rate=16000, 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
-
 def extract_text_features(text):
     inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
     outputs = text_model(**inputs)
@@ -32,16 +18,13 @@ def extract_text_features(text):
     return "Hate Speech" if predicted_class >= 1 else "Not Hate Speech"
 
 def predict(text_input):
-    audio_path = "input.opus"
-    transcribed_text = transcribe(audio_path)
-    prediction = extract_text_features(text_input or transcribed_text)
-    if text_input:
-        return f"Predicted: {prediction}"
-    else:
-        return f"Predicted: {prediction} \n\n(Transcribed: {transcribed_text})"
+    if not text_input:
+        return "Please enter some text."
+    prediction = extract_text_features(text_input)
+    return f"Predicted: {prediction}"
 
 st.title("Hate Speech Detector")
-text_input = st.text_input("Enter text (optional):")
-if st.button("Run Prediction"):
+text_input = st.text_input("Enter text:")
+if st.button("Predict"):
     result = predict(text_input)
     st.success(result)