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# App
# Import required libraries
import os
import pandas as pd
import streamlit as st
from transformers import pipeline
from sentence_transformers import SentenceTransformer, util
import requests
import json
from pyngrok import ngrok
# Set up Hugging Face API token
api_key = os.getenv("HF_API_KEY") # Replace with your Hugging Face API token
# Load the CSV dataset
data = pd.read_csv('/content/genetic_diseases_dataset.csv')
# Initialize Sentence Transformer model for RAG-based retrieval
retriever_model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
# Create embeddings for the entire dataset for retrieval
# data['embeddings'] = data['description'].apply(lambda x: retriever_model.encode(x))
# Drop unnecessary columns (Unnamed columns)
data = data.drop(columns=['Unnamed: 0', 'Unnamed: 11', 'Unnamed: 12', 'Unnamed: 13'])
# Combine relevant columns into one combined description field
data['combined_description'] = (
data['Symptoms'].fillna('') + " " +
data['Severity Level'].fillna('') + " " +
data['Risk Assessment'].fillna('') + " " +
data['Treatment Options'].fillna('') + " " +
data['Suggested Medical Tests'].fillna('') + " " +
data['Minimum Values for Medical Tests'].fillna('') + " " +
data['Emergency Treatment'].fillna('')
)
# Initialize the Sentence Transformer model for embeddings
retriever_model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
# Function to safely generate embeddings for each row
def generate_embedding(description):
if description: # Check if the description is not empty or NaN
return retriever_model.encode(description).tolist() # Convert the numpy array to list
else:
return []
# Generate embeddings for the combined description
data['embeddings'] = data['combined_description'].apply(generate_embedding)
# Function to retrieve relevant information from CSV dataset based on user query
def get_relevant_info(query, top_k=3):
query_embedding = retriever_model.encode(query)
similarities = [util.cos_sim(query_embedding, doc_emb)[0][0].item() for doc_emb in data['embeddings']]
top_indices = sorted(range(len(similarities)), key=lambda i: similarities[i], reverse=True)[:top_k]
return data.iloc[top_indices]
# Function to generate response using Hugging Face Model API
def generate_response(input_text):
api_url = "https://api-inference.huggingface.co/models/m42-health/Llama3-Med42-8B"
headers = {"Authorization": f"Bearer {os.environ['HUGGINGFACEHUB_API_TOKEN']}"}
payload = {"inputs": input_text}
response = requests.post(api_url, headers=headers, json=payload)
return json.loads(response.content.decode("utf-8"))[0]["generated_text"]
# Streamlit UI for the Chatbot
def main():
st.title("Medical Report and Analysis Chatbot")
st.sidebar.header("Upload Medical Report or Enter Query")
# Text input for user queries
user_query = st.sidebar.text_input("Type your question or query")
# File uploader for medical report
uploaded_file = st.sidebar.file_uploader("Upload a medical report (optional)", type=["txt", "pdf", "csv"])
# Process the query if provided
if user_query:
st.write("### Query Response:")
# Retrieve relevant information from dataset
relevant_info = get_relevant_info(user_query)
st.write("#### Relevant Medical Information:")
for i, row in relevant_info.iterrows():
st.write(f"- {row['description']}")
# Generate a response from the Llama3-Med42-8B model
response = generate_response(user_query)
st.write("#### Model's Response:")
st.write(response)
# Process the uploaded file (if any)
if uploaded_file:
# Display analysis of the uploaded report file
st.write("### Uploaded Report Analysis:")
report_text = "Extracted report content here" # Placeholder for file processing logic
st.write(report_text)
if __name__ == "__main__":
main()
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