app.py

# git clone https://huggingface.co/spaces/Everwell-KWK/Everwell
import gradio as gr
import random
from huggingface_hub import InferenceClient
from datasets import load_dataset
from sentence_transformers import SentenceTransformer
import torch
import pandas as pd

file_path = "drug list for ai medication chatbot - Sheet1"
dataset = load_dataset("csv", data_files="drug list for ai medication chatbot - Sheet1.csv")

with open("drug list for ai medication chatbot - Sheet1.csv", "r", encoding = "utf-8") as file:
    file_path = file.read()
    # print(file_path)

client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
messages = [{"role": "system", "content":"You are a chatbot specializing in helping the user track medications. This applies to both short-term medications such as antibiotics and like, and long-term medications like antidepressants and beta-blockers"}]

cleaned_drug_list = file_path.strip()
chunked_list = cleaned_drug_list.split("\n")
cleaned_chunks = []

for chunked in chunked_list: #loops through database & adds it to the modifications done by chunked_list
    stripped_list = chunked.strip()
    if stripped_list:
        cleaned_chunks.append(stripped_list) # appends to empty in cleaned_chunks = []
#print(cleaned_chunks)
df = pd.read_csv("drug list for ai medication chatbot - Sheet1.csv")
print(df)

def respond(message, history):
    if not history:
        return "Hello! How can I assist you today?"

    messages.append({"role": "user", "content": message})
    response = client.chat_completion(
        messages,
        max_tokens=10000
    )

    # Extract response safely
    try:
        return response['choices'][0]['message']['content'].strip()
    except Exception as e:
        return f"Sorry, something went wrong: {e}"



chatbot = gr.ChatInterface(
    respond,
    type="messages",
    title="EverWell",
    theme=gr.themes.Glass(),
    examples=[
        ["What medication am I on?"],
        ["How much medication do I have left?"],
        ["Can we chat?"]
    ],
    cache_examples=False  # <- important!
)

chatbot.launch()
# When chatbot is opened, it needs to greet the user with a generated response like "Hello, User! What medication can I help you figure out today?"