Update app.py

app.py

@@ -11,7 +11,7 @@ from llama_index.llms import HuggingFaceLLM
 from langchain.document_loaders import PyPDFLoader
 
 
-**Mount Google Drive to access data (you may need to authenticate)**
+
 
 import pandas as pd
 from datasets import load_dataset, concatenate_datasets
@@ -34,7 +34,7 @@ combined_dataset = concatenate_datasets(datasets)
 #from google.colab import drive
 #drive.mount('/content/drive')
 
-**Reading the data from the saved path in google drive**
+
 
 #documents = SimpleDirectoryReader("/content/drive/MyDrive/Data").load_data()
 
@@ -42,24 +42,23 @@ from langchain.text_splitter import CharacterTextSplitter
 from langchain import OpenAI
 from langchain.document_loaders import PyPDFLoader
 
-**Define a system prompt for the Q&A assistant**
+
 
 
 from llama_index.prompts.prompts import SimpleInputPrompt
 
 
-system_prompt = "You are a medical AI chatbot. Your goal is to answer questions as accurately as possible based on the instructions and context provided.Use only information from the previous context information. Do not invent stuff or give false information"
+system_prompt = "You are a medical chatbot designed to provide general medical information and guidance to patients. You are not a substitute for a licensed healthcare professional. Please always consult with a doctor or other qualified healthcare provider for any medical concerns. Here are some specific examples of how you can be helpful: Answer general medical questions about symptoms, conditions, and treatments, Provide information on healthy lifestyle habits and preventive care, Help patients find reliable medical resources and support groups, Offer emotional support and reassurance to patients facing medical challenges. Please keep in mind the following: Always direct patients to seek professional medical attention if they have serious concerns, Use clear and concise language that is easy for patients to understand. Be respectful and sensitive to patients individual needs and concerns.I am confident that you can be a valuable tool for patients seeking medical information and guidance. Thank you for your dedication to helping others."
 
 
 
 # This will wrap the default prompts that are internal to llama-index
 query_wrapper_prompt = SimpleInputPrompt("<|USER|>{query_str}<|ASSISTANT|>")
 
-**Log in to Hugging Face**
 
 #!huggingface-cli login
 
-**Configure the HuggingFaceLLM (Language Model)**
+
 
 import torch
 
@@ -75,7 +74,7 @@ llm = HuggingFaceLLM(
     # uncomment this if using CUDA to reduce memory usage
     model_kwargs={"torch_dtype": torch.float16 , "load_in_8bit":True})
 
-**Configure embeddings using Hugging Face model**
+
 
 from langchain.embeddings.huggingface import HuggingFaceEmbeddings
 from llama_index import LangchainEmbedding, ServiceContext
@@ -85,7 +84,6 @@ embed_model = LangchainEmbedding(
 )
 
 
-**Configure the service context**
 
 service_context = ServiceContext.from_defaults(
     chunk_size=1024,
@@ -93,18 +91,18 @@ service_context = ServiceContext.from_defaults(
     embed_model=embed_model
 )
 
-**Create a vector store index from the loaded documents**
+
 
 index = VectorStoreIndex.from_documents(combined_dataset, service_context=service_context)
 
-**Create a query engine for the index**
+
 
 query_engine = index.as_query_engine()
 response = query_engine.query("What is gross profit?")
 
 print(response)
 
-**To interact with the chatbot**
+
 
 import gradio as gr