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| # 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") | |
| # Load the CSV dataset | |
| data = pd.read_csv('genetic-Final.csv') | |
| # 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['combined_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) | |
| # Start Streamlit app in Colab using ngrok | |
| if __name__ == "__main__": | |
| main() | |