Update app.py

app.py

@@ -1,39 +1,44 @@
-import torch
-import librosa
-import gradio as gr
-from transformers import WhisperProcessor, WhisperForConditionalGeneration, AutoTokenizer, AutoModelForCausalLM
-
-# Load models from the Space or from Hugging Face Hub
-whisper_model = WhisperForConditionalGeneration.from_pretrained("donnamae/whisper-finetuned-cebuano-accent", use_auth_token=True)
-whisper_processor = WhisperProcessor.from_pretrained("donnamae/whisper-finetuned-cebuano-accent", use_auth_token=True)
-
-code_tokenizer = AutoTokenizer.from_pretrained("meta-llama/CodeLlama-7b-Instruct-hf")
-code_model = AutoModelForCausalLM.from_pretrained("meta-llama/CodeLlama-7b-Instruct-hf").to("cuda" if torch.cuda.is_available() else "cpu")
-
-def transcribe_and_generate(audio):
-    audio_data, sr = librosa.load(audio, sr=16000)
-    input_features = whisper_processor(audio_data, sampling_rate=sr, return_tensors="pt").input_features
-    predicted_ids = whisper_model.generate(input_features)
-    transcription = whisper_processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
-
-    # Format prompt for code generation
-    prompt = f"# Task: {transcription.strip()}\n\n```python\n"
-    inputs = code_tokenizer(prompt, return_tensors="pt").to(code_model.device)
-
-    outputs = code_model.generate(**inputs, max_length=256)
-    generated_text = code_tokenizer.decode(outputs[0], skip_special_tokens=True)
-
-    # Extract clean code
-    generated_code = generated_text.replace(prompt, "").strip().split("```")[0]
-
-    return transcription, generated_code
-
-demo = gr.Interface(
-    fn=transcribe_and_generate,
-    inputs=gr.Audio(type="filepath"),
-    outputs=[gr.Text(label="Transcribed Command"), gr.Code(label="Generated Code")],
-    title="Voice-to-Code Generator",
-    description="Speak your coding command. The system will transcribe and generate the corresponding code."
-)
-
-demo.launch()
+import torch
+import librosa
+import gradio as gr
+from transformers import WhisperProcessor, WhisperForConditionalGeneration, AutoTokenizer, AutoModelForCausalLM
+
+# Load models from the Space or from Hugging Face Hub
+whisper_model = WhisperForConditionalGeneration.from_pretrained("donnamae/whisper-finetuned-cebuano-accent", token=True)
+whisper_processor = WhisperProcessor.from_pretrained("donnamae/whisper-finetuned-cebuano-accent", token=True)
+
+code_tokenizer = AutoTokenizer.from_pretrained("meta-llama/CodeLlama-7b-Instruct-hf")
+code_model = AutoModelForCausalLM.from_pretrained(
+    "meta-llama/CodeLlama-7b-Instruct-hf", 
+    torch_dtype="auto",
+    device_map="auto",
+    trust_remote_code=True
+).to("cuda" if torch.cuda.is_available() else "cpu")
+
+def transcribe_and_generate(audio):
+    audio_data, sr = librosa.load(audio, sr=16000)
+    input_features = whisper_processor(audio_data, sampling_rate=sr, return_tensors="pt").input_features
+    predicted_ids = whisper_model.generate(input_features)
+    transcription = whisper_processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
+
+    # Format prompt for code generation
+    prompt = f"# Task: {transcription.strip()}\n\n```python\n"
+    inputs = code_tokenizer(prompt, return_tensors="pt").to(code_model.device)
+
+    outputs = code_model.generate(**inputs, max_length=256)
+    generated_text = code_tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+    # Extract clean code
+    generated_code = generated_text.replace(prompt, "").strip().split("```")[0]
+
+    return transcription, generated_code
+
+demo = gr.Interface(
+    fn=transcribe_and_generate,
+    inputs=gr.Audio(type="filepath"),
+    outputs=[gr.Text(label="Transcribed Command"), gr.Code(label="Generated Code")],
+    title="Voice-to-Code Generator",
+    description="Speak your coding command. The system will transcribe and generate the corresponding code."
+)
+
+demo.launch()