Create app.py

app.py

@@ -0,0 +1,148 @@
+import time
+import torch
+import requests
+import gradio as gr
+from transformers import pipeline
+from usellm import Message, Options, UseLLM
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from transformers import WhisperProcessor, WhisperForConditionalGeneration
+
+
+def text_to_speech(text_input):
+    CHUNK_SIZE = 1024
+    url = "https://api.elevenlabs.io/v1/text-to-speech/TxGEqnHWrfWFTfGW9XjX"
+    
+    headers = {
+        "Accept": "audio/mpeg",
+        "Content-Type": "application/json",
+        "xi-api-key": "7f91dfdd5390bbfd9d44148c59644039"
+    }
+    
+    data = {
+        "text": text_input,
+        "model_id": "eleven_monolingual_v1"
+    }
+    
+    audio_write_path = f"""output_{int(time.time())}.mp3"""
+    
+    response = requests.post(url, json=data, headers=headers)
+    with open(audio_write_path, 'wb') as f:
+        for chunk in response.iter_content(chunk_size=CHUNK_SIZE):
+            if chunk:
+                f.write(chunk)
+
+    return audio_write_path
+
+
+def whisper_inference(input_audio):
+    
+    processor1 = WhisperProcessor.from_pretrained("openai/whisper-large-v2")
+    model1 = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large-v2")
+    
+    forced_decoder_ids = processor1.get_decoder_prompt_ids(task="translate")
+    
+    input_features = processor1(input_audio, sampling_rate=16000, return_tensors="pt").input_features
+    predicted_ids = model1.generate(input_features, forced_decoder_ids=forced_decoder_ids)
+    transcription = processor1.batch_decode(predicted_ids, skip_special_tokens=True)
+    
+    return transcription
+
+
+
+def biogpt_large_infer(input_text):
+    
+    tokenizer1 = AutoTokenizer.from_pretrained("microsoft/BioGPT-Large-PubMedQA", add_special_tokens=False)
+    model = AutoModelForCausalLM.from_pretrained("microsoft/BioGPT-Large-PubMedQA")#.to('cuda:0')
+    
+    generator = pipeline("text-generation", model=model, tokenizer=tokenizer1)#, device="cuda:0")
+    output = generator(input_text, min_length=100,max_length=1024,num_beams=5,early_stopping=True, 
+                       num_return_sequences=1, do_sample=True)
+    output = output[0]['generated_text']
+    output = output.replace('▃','').replace('FREETEXT','').replace('TITLE','').replace('PARAGRAPH','').replace('ABSTRACT','').replace('<','').replace('>','').replace('/','').strip()
+    
+    return output
+
+
+    
+def chatgpt_infer(input_text):
+
+    # Initialize the service
+    service = UseLLM(service_url="https://usellm.org/api/llm")
+
+    # Prepare the conversation
+    messages = [
+      Message(role="system", content="You are a medical assistant, which answers the query based on factual medical information only."),
+      Message(role="user", content=f"Give me few points on the disease {input_text} and its treatment."),
+    ]
+    
+    options = Options(messages=messages)
+
+    # Interact with the service
+    response = service.chat(options)
+    
+    return response.content
+
+
+def audio_interface_demo(input_audio):
+    
+    en_prompt = whisper_inference(input_audio)
+    
+    biogpt_output = biogpt_large_infer(en_prompt)
+    chatgpt_output = chatgpt_infer(en_prompt)
+    
+    bio_audio_output = text_to_speech(biogpt_output)
+    chat_audio_output = text_to_speech(chatgpt_output)
+            
+    return biogpt_output, chatgpt_output, bio_audio_output, chat_audio_output
+
+
+def text_interface_demo(input_text):
+    
+    #en_prompt = whisper_inference(input_audio)
+    
+    biogpt_output = biogpt_large_infer(input_text)
+    chatgpt_output = chatgpt_infer(input_text)
+            
+    return biogpt_output, chatgpt_output
+
+
+examples = [
+    ["Meningitis is"],
+    ["Brain Tumour is"]  
+]
+
+app = gr.Blocks()
+with app:
+    gr.Markdown("# **<h4 align='center'>Voice based Medical Informational Bot<h4>**")
+     
+    with gr.Row():
+        with gr.Column():
+            
+            with gr.Tab("Text"):
+                input_text = gr.Textbox(lines=3, value="Brain Tumour is", label="Text")
+                text_button = gr.Button(value="Predict")
+
+            with gr.Tab("Audio"):
+                input_audio = gr.Audio(value="/home/ec2-user/SageMaker/text_vd_demo/artifacts/input.mp3", source="upload", type="filepath", label='Audio')
+                audio_button = gr.Button(value="Predict")  
+                  
+    with gr.Row():
+        with gr.Column():
+            with gr.Tab("Output Text"):
+
+                biogpt_output = gr.Textbox(lines=3, label="BioGpt Output")
+                chatgpt_output = gr.Textbox(lines=3,label="ChatGPT Output")
+
+            with gr.Tab("Output Audio"):
+
+                biogpt_output = gr.Textbox(lines=3, label="BioGpt Output")
+                chatgpt_output = gr.Textbox(lines=3,label="ChatGPT Output")
+
+                audio_output1 = gr.Audio(value=None, label="ChatGPT Audio Output")
+                audio_output2 = gr.Audio(value=None, label="BioGpt Audio Output")
+                    
+    #gr.Examples(examples, inputs=[input_text], outputs=[prompt_text, output_text, translated_text], fn=biogpt_text, cache_examples=False)
+    text_button.click(text_interface_demo, inputs=[input_text], outputs=[biogpt_output, chatgpt_output])
+    audio_button.click(audio_interface_demo, inputs=[input_audio], outputs=[biogpt_output, chatgpt_output, audio_output2, audio_output1])
+    
+app.launch(share=True, debug=True)