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# -*- coding: utf-8 -*-
import os
import gradio as gr
import whisper
from gtts import gTTS
from groq import Groq
import pandas as pd
import numpy as np
from sentence_transformers import SentenceTransformer
import faiss
index_file_path="faiss_index.index"
embeddings_file_path="embeddings.npy"
# Load Whisper model for transcription
model = whisper.load_model("base")
# Set up Groq API client (make sure your API key is correct)
client = Groq(api_key="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")
# Load the dataset
df = pd.read_json("hf://datasets/Amod/mental_health_counseling_conversations/combined_dataset.json", lines=True)
corpus = df['Context'].dropna().tolist()
# Initialize SentenceTransformer to generate embeddings
embedder = SentenceTransformer('paraphrase-MiniLM-L6-v2')
# Function to load or build the FAISS index
def load_or_build_index():
if os.path.exists(index_file_path) and os.path.exists(embeddings_file_path):
print("Loading existing index and embeddings...")
index = faiss.read_index(index_file_path)
embeddings = np.load(embeddings_file_path)
else:
print("Building new index...")
embeddings = embedder.encode(corpus, convert_to_numpy=True)
dimension = embeddings.shape[1]
index = faiss.IndexFlatL2(dimension) # FAISS index for L2 (Euclidean) distance
index.add(embeddings)
faiss.write_index(index, index_file_path) # Save the index to disk
np.save(embeddings_file_path, embeddings) # Save embeddings to disk
return index, embeddings
# Load or build the FAISS index
index, corpus_embeddings = load_or_build_index()
# Function to retrieve the most relevant context from the corpus using FAISS
def retrieve_relevant_context(user_input):
user_input_embedding = embedder.encode([user_input]) # Convert the user's query into an embedding
k = 1 # Retrieve the top 1 most relevant document
D, I = index.search(user_input_embedding, k) # Perform the search in the FAISS index
return corpus[I[0][0]] # Return the most relevant document
# Function to process the audio input, retrieve context, and generate a response
def chatbot(audio):
# Transcribe the audio input using Whisper
transcription = model.transcribe(audio)
user_input = transcription["text"]
# Retrieve the most relevant context from the dataset using the vector database (FAISS)
relevant_context = retrieve_relevant_context(user_input)
# Generate a response using the Groq API with Llama 8B, including relevant context
chat_completion = client.chat.completions.create(
messages=[
{"role": "user", "content": user_input},
{"role": "system", "content": f"Context: {relevant_context}"}
],
model="llama3-8b-8192"
)
# Extract the generated response text
response_text = chat_completion.choices[0].message.content
# Convert the response text to speech using gTTS
tts = gTTS(text=response_text, lang='en')
tts.save("response.mp3")
return response_text, "response.mp3"
# Create a custom Gradio interface
def build_interface():
with gr.Blocks() as demo:
gr.Markdown(
"""
<h1 style="text-align: center; color: #4CAF50;">Chill Parents</h1>
<h3 style="text-align: center;">Chatbot to help parents and other family members to reduce stress between them</h3>
<p style="text-align: center;">Talk to the AI-powered chatbot and get responses in real-time. Start by recording your voice.</p>
"""
)
with gr.Row():
with gr.Column(scale=1):
audio_input = gr.Audio(type="filepath", label="Record Your Voice")
with gr.Column(scale=2):
chatbot_output_text = gr.Textbox(label="Chatbot Response")
chatbot_output_audio = gr.Audio(label="Audio Response")
submit_button = gr.Button("Submit")
submit_button.click(chatbot, inputs=audio_input, outputs=[chatbot_output_text, chatbot_output_audio])
return demo
# Launch the interface
if __name__ == "__main__":
interface = build_interface()
interface.launch()
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