Create app.py

app.py

@@ -0,0 +1,63 @@
+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, AutoProcessor, AutoModel
+import scipy.io.wavfile as wav
+import numpy as np
+
+# Load models and tokenizers
+story_tokenizer = AutoTokenizer.from_pretrained("starmpcc/Asclepius-Llama3-8B", use_fast=False)
+story_model = AutoModelForCausalLM.from_pretrained("starmpcc/Asclepius-Llama3-8B")
+
+speech_processor = AutoProcessor.from_pretrained("suno/bark-small")
+speech_model = AutoModel.from_pretrained("suno/bark-small")
+
+# Function to generate story
+def generate_story(prompt):
+    model_input = f"""You are an intelligent clinical language model.
+    Below is a snippet of patient's discharge summary and a following instruction from healthcare professional.
+    Write a response that appropriately completes the instruction.
+    The response should provide the accurate answer to the instruction, while being concise.
+
+    [Discharge Summary Begin]
+    {prompt}
+    [Discharge Summary End]
+
+    [Instruction Begin]
+    Generate a short story based on this prompt.
+    [Instruction End] 
+    """
+    
+    input_ids = story_tokenizer(model_input, return_tensors="pt").input_ids
+    output = story_model.generate(input_ids, max_length=500)
+    return story_tokenizer.decode(output[0], skip_special_tokens=True)
+
+# Function to generate speech
+def generate_speech(text, speaker="v2/en_speaker_6"):
+    inputs = speech_processor(text, voice_preset=speaker, return_tensors="pt")
+    speech_values = speech_model.generate(**inputs, do_sample=True)
+
+    audio_array = speech_values.cpu().numpy().squeeze()
+    sample_rate = speech_model.generation_config.sample_rate
+
+    return (sample_rate, audio_array)
+
+# Gradio interface function
+def text_to_speech(prompt):
+    story = generate_story(prompt)
+    audio = generate_speech(story)
+    return story, audio
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=text_to_speech,
+    inputs=gr.Textbox(label="Enter story prompt"),
+    outputs=[
+        gr.Textbox(label="Generated Story"),
+        gr.Audio(label="Generated Speech")
+    ],
+    title="Story Generator and Text-to-Speech",
+    description="Enter a prompt to generate a story, then convert it to speech."
+)
+
+# Launch the app
+iface.launch()