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import streamlit as st
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
from transformers import GPT2Tokenizer, GPT2LMHeadModel
from sentence_transformers import SentenceTransformer, util
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import gdown
import os
import pandas as pd
excel_file_path = 'medical_data.csv'
try:
medical_df = pd.read_csv(excel_file_path, encoding='utf-8')
except UnicodeDecodeError:
medical_df = pd.read_csv(excel_file_path, encoding='latin1')
# TF-IDF Vectorization
vectorizer = TfidfVectorizer(stop_words='english')
X_tfidf = vectorizer.fit_transform(medical_df['Questions'])
tokenizer = AutoTokenizer.from_pretrained("Josephgflowers/TinyLlama-3T-Cinder-v1.3")
model = AutoModelForCausalLM.from_pretrained("Josephgflowers/TinyLlama-3T-Cinder-v1.3")
# Load pre-trained Sentence Transformer model
sbert_model_name = "paraphrase-MiniLM-L6-v2"
sbert_model = SentenceTransformer(sbert_model_name)
# Function to answer medical questions using a combination of TF-IDF, LLM, and semantic similarity
def get_medical_response(question, vectorizer, X_tfidf, model, tokenizer, sbert_model, medical_df):
# TF-IDF Cosine Similarity
question_vector = vectorizer.transform([question])
tfidf_similarities = cosine_similarity(question_vector, X_tfidf).flatten()
# Find the most similar question using semantic similarity
question_embedding = sbert_model.encode(question, convert_to_tensor=True)
similarities = util.pytorch_cos_sim(question_embedding, sbert_model.encode(medical_df['Questions'].tolist(), convert_to_tensor=True)).flatten()
max_sim_index = similarities.argmax().item()
# LLM response generation
input_text = "DiBot: " + medical_df.iloc[max_sim_index]['Questions']
input_ids = tokenizer.encode(input_text, return_tensors="pt")
attention_mask = torch.ones(input_ids.shape, dtype=torch.long)
pad_token_id = tokenizer.eos_token_id
lm_output = model.generate(input_ids, max_length=150, num_return_sequences=1, no_repeat_ngram_size=2, attention_mask=attention_mask, pad_token_id=pad_token_id)
lm_generated_response = tokenizer.decode(lm_output[0], skip_special_tokens=True)
# Compare similarities and choose the best response
if tfidf_similarities.max() > 0.5:
tfidf_index = tfidf_similarities.argmax()
return medical_df.iloc[tfidf_index]['Answers']
else:
return lm_generated_response
# Custom CSS to enhance the UI
st.markdown("""
<style>
.main {
background-color: ##131313;
padding: 2rem;
border-radius: 10px;
}
.stTextInput > div > div > input {
border: 2px solid #ccc;
border-radius: 10px;
padding: 10px;
width: 100%;
}
.stButton > button {
background-color: #4CAF50;
color: white;
border: none;
border-radius: 10px;
padding: 10px 20px;
cursor: pointer;
}
.stButton > button:hover {
background-color: #45a049;
}
.stTextArea > div > div > textarea {
border: 2px solid #ccc;
border-radius: 10px;
padding: 10px;
width: 100%;
}
</style>
""", unsafe_allow_html=True)
# Streamlit app layout
st.title("🤖 DiBot - Your Medical Query Assistant")
st.write("Ask me any medical question, and I'll do my best to provide an accurate response.")
st.subheader("Enter your question below:")
user_input = st.text_input("Your question:", "")
if user_input.lower() == "exit":
st.stop()
if user_input:
response = get_medical_response(user_input, vectorizer, X_tfidf, model, tokenizer, sbert_model, medical_df)
st.subheader("Bot's Response:")
st.text_area("", response, height=200)
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