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import streamlit as st
import os
import requests
# Ensure that the Groq API key is set
os.environ["GROQ_API_KEY"] = "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"
# Groq API endpoint
GROQ_API_URL = "https://api.groq.com/v1/inference"
# Function to perform embedding retrieval using MiniLM via Groq API
def retrieve_embedding(user_query):
payload = {
"model": "microsoft/MiniLM-L6-H384-uncased",
"input_text": user_query
}
headers = {
"Authorization": f"Bearer {os.getenv('GROQ_API_KEY')}"
}
response = requests.post(f"{GROQ_API_URL}/embedding", json=payload, headers=headers)
return response.json()["embedding"]
# Function to perform response generation using FLAN-T5 via Groq API
def generate_response(context):
payload = {
"model": "google/flan-t5-small",
"input_text": f"Given the following context, provide a supportive response: {context}"
}
headers = {
"Authorization": f"Bearer {os.getenv('GROQ_API_KEY')}"
}
response = requests.post(f"{GROQ_API_URL}/generate", json=payload, headers=headers)
return response.json()["text"]
# Load the counseling conversations dataset
from datasets import load_dataset
dataset = load_dataset("Amod/mental_health_counseling_conversations")["train"]
# Precompute embeddings for the dataset responses using Groq API
@st.cache(allow_output_mutation=True)
def embed_dataset(dataset):
embeddings = []
for entry in dataset:
embedding = retrieve_embedding(entry["response"])
embeddings.append(embedding)
return embeddings
dataset_embeddings = embed_dataset(dataset)
# Function to retrieve closest responses from the dataset using cosine similarity
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np
def retrieve_response(user_query, dataset, dataset_embeddings, k=5):
query_embedding = retrieve_embedding(user_query)
cos_scores = cosine_similarity([query_embedding], dataset_embeddings)[0]
top_indices = np.argsort(cos_scores)[-k:][::-1]
retrieved_responses = []
for idx in top_indices:
retrieved_responses.append(dataset[idx]["response"])
return retrieved_responses
# Streamlit app UI
st.title("Emotional Support Buddy")
st.write("Enter your thoughts or concerns, and I'll provide some comforting words.")
# User input
user_query = st.text_input("How are you feeling today?")
if user_query:
# Retrieve similar responses from the dataset
retrieved_responses = retrieve_response(user_query, dataset, dataset_embeddings)
# Join retrieved responses to create a supportive context
context = " ".join(retrieved_responses)
# Generate a supportive response using FLAN-T5 via Groq API
supportive_response = generate_response(context)
st.write("Here's some advice or support for you:")
st.write(supportive_response)
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