import pandas as pd


df = pd.read_csv('./drugs_side_effects_drugs_com.csv')

df = df[['drug_name', 'medical_condition', 'side_effects']]
df.dropna(inplace=True)

context_data = []
for i in range(500):
  context = ""
  for j in range(3):
    context += df.columns[j]
    context += ": "
    context += str(df.iloc[i][j])
    context += " "
  context_data.append(context)

import os

# Get the secret key from the environment
groq_key = os.environ.get('gloq_key')

## LLM used for RAG
from langchain_groq import ChatGroq

llm = ChatGroq(model="llama-3.1-70b-versatile",api_key=groq_key)

## Embedding model!
from langchain_huggingface import HuggingFaceEmbeddings
embed_model = HuggingFaceEmbeddings(model_name="mixedbread-ai/mxbai-embed-large-v1")

# create vector store!
from langchain_chroma import Chroma

vectorstore = Chroma(
    collection_name="medical_dataset_store",
    embedding_function=embed_model,
    persist_directory="./",
)

# add data to vector nstore
vectorstore.add_texts(context_data)

retriever = vectorstore.as_retriever()

from langchain_core.prompts import PromptTemplate

template = ("""You are a pharmacist and medical expert.
    Use the provided context to answer the question.
    If you don't know the answer, say so. Explain your answer in detail.
    Do not discuss the context in your response; just provide the answer directly.

    Context: {context}

    Question: {question}

    Answer:""")

rag_prompt = PromptTemplate.from_template(template)

from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough

rag_chain = (
    {"context": retriever, "question": RunnablePassthrough()}
    | rag_prompt
    | llm
    | StrOutputParser()
)


import gradio as gr

# Function to stream responses
def rag_memory_stream(text):
    partial_text = ""
    for new_text in rag_chain.stream(text):  # Assuming rag_chain is pre-defined
        partial_text += new_text
        yield partial_text

# Title and description for the app
title = "AI Medical Assistant for Drug Information and Side Effects"
description = """
This AI-powered chatbot is designed to provide reliable information about drugs, their side effects, and related medical conditions.
It utilizes the Groq API and LangChain to deliver real-time, accurate responses. 

Ask questions like:
1. What are the side effects of taking aspirin daily?
2. What is the recommended treatment for a common cold?
3. What is the disease for constant fatigue and muscle weakness?
4. What are the symptoms of diabetes?
5. How can hypertension be managed?

**Disclaimer:** This chatbot is for informational purposes only and is not a substitute for professional medical advice.
"""

# Customizing Gradio interface for a better look
demo = gr.Interface(
    fn=rag_memory_stream,
    inputs=gr.Textbox(
        lines=2, 
        placeholder="Type your medical question here...", 
        label="Your Medical Question"
    ),
    outputs=gr.Textbox(
        lines=10, 
        label="AI Response"
    ),
    title=title,
    description=description,
    theme="compact",  # Adding a compact theme for a polished look
    allow_flagging="never"
)

# # Launching the app
# demo.launch(share=True)

# import gradio as gr

# def rag_memory_stream(text):
#     partial_text = ""
#     for new_text in rag_chain.stream(text):
#         partial_text += new_text
#         yield partial_text

# examples = ['I feel dizzy', 'what is the possible sickness for fatigue']




# title = "Real-time AI App with Groq API and LangChain to Answer medical questions"
# demo = gr.Interface(
#     title=title,
#     fn=rag_memory_stream,
#     inputs="text",
#     outputs="text",
#     examples=examples,
#     allow_flagging="never",
# )


if __name__ == "__main__":
    demo.launch()