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import os
import gradio as gr
import pathlib
import torch
import faiss
from sentence_transformers import SentenceTransformer
from brain import encode_image, analyze_image_with_query
from patientvoice import record_audio, transcribe_with_groq
from doctorvoice import text_to_speech_with_gtts, text_to_speech_with_elevenlabs
from dotenv import load_dotenv
load_dotenv()
from langchain_community.vectorstores import FAISS
from langchain_core.embeddings import Embeddings
from langchain_core.prompts import ChatPromptTemplate
from langchain_community.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
# Check if CUDA is available
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Using device: {device}")
# Initialize embeddings model
class SentenceTransformerEmbeddings(Embeddings):
def __init__(self, model_name: str, device: str = None):
self.model = SentenceTransformer(model_name, device=device)
def embed_documents(self, texts: list[str]) -> list[list[float]]:
embeddings = self.model.encode(texts, convert_to_tensor=False)
return embeddings.tolist()
def embed_query(self, text: str) -> list[float]:
embedding = self.model.encode(text, convert_to_tensor=False)
return embedding.tolist()
embeddings = SentenceTransformerEmbeddings(
model_name="sentence-transformers/all-MiniLM-L6-v2",
device=device
)
def create_vectorstore():
# Define vectorstore paths consistently
VECTORSTORE_DIR = "vectorstore/db_faiss"
vectorstore_path = pathlib.Path(VECTORSTORE_DIR)
# Create vectorstore directory if it doesn't exist
vectorstore_path.mkdir(parents=True, exist_ok=True)
if not (vectorstore_path / "index.faiss").exists():
print("Creating new vectorstore...")
# Load and split the PDF
loader = PyPDFLoader("medical.pdf")
documents = loader.load()
# Split documents into chunks
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=2000,
chunk_overlap=100,
length_function=len,
)
texts = text_splitter.split_documents(documents)
# Create and save the vectorstore
vectorstore = FAISS.from_documents(texts, embeddings)
# If CUDA is available, convert index to GPU
if device == "cuda":
res = faiss.StandardGpuResources() # Initialize GPU resources
index = vectorstore.index
gpu_index = faiss.index_cpu_to_gpu(res, 0, index) # Move to GPU
vectorstore.index = gpu_index
# Save the vectorstore
vectorstore.save_local(VECTORSTORE_DIR)
print("Vectorstore created and saved successfully.")
else:
print("Loading existing vectorstore...")
# Load existing vectorstore
vectorstore = FAISS.load_local(
folder_path=VECTORSTORE_DIR,
embeddings=embeddings,
allow_dangerous_deserialization=True
)
# If CUDA is available, convert loaded index to GPU
if device == "cuda":
res = faiss.StandardGpuResources() # Initialize GPU resources
index = vectorstore.index
gpu_index = faiss.index_cpu_to_gpu(res, 0, index) # Move to GPU
vectorstore.index = gpu_index
print("Vectorstore loaded successfully.")
def get_relevant_context(query):
try:
# Search the vector store for relevant documents
docs = vectorstore.similarity_search(query, k=2)
# Extract and combine the content from retrieved documents
context = "\n".join([doc.page_content for doc in docs])
context = "Use the following medical context to inform your response: " + context
return context if not context else ""
except Exception as e:
print(f"Error in similarity search: {e}")
return "Could not retrieve relevant context."
# Update system prompt to include retrieved context
def get_enhanced_prompt(query, context):
enhanced_prompt = f"""### **Patient Information**:
**Patient Query**: {query}
{context}
"""
return enhanced_prompt
def process_inputs(audio_filepath, image_filepath):
speech_to_text_output = transcribe_with_groq(GROQ_API_KEY=os.environ.get("GROQ_API_KEY"),
audio_filepath=audio_filepath,
stt_model="whisper-large-v3")
# Get relevant context from the vector store
context = get_relevant_context(speech_to_text_output)
# Handle the image input
if image_filepath:
enhanced_prompt = get_enhanced_prompt(speech_to_text_output, context)
doctor_response = analyze_image_with_query(query=enhanced_prompt, encoded_image=encode_image(image_filepath), model="llama-3.2-90b-vision-preview")
else:
doctor_response = "No image provided for me to analyze"
# Generate audio response and return the filepath
output_filepath = "output_audio.mp3"
voice_of_doctor = text_to_speech_with_elevenlabs(input_text=doctor_response, output_filepath=output_filepath)
return speech_to_text_output, doctor_response, output_filepath
# Create the interface
iface = gr.Interface(
fn=process_inputs,
inputs=[
gr.Audio(sources=["microphone"], type="filepath"),
gr.Image(type="filepath")
],
outputs=[
gr.Textbox(label="Speech to Text"),
gr.Textbox(label="Doctor's Response"),
gr.Audio(label="Doctor's Voice")
],
title="AI Doctor with Vision and Voice"
)
iface.launch(debug=True) |