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import streamlit as st
from langchain import memory as lc_memory
from langsmith import Client
from streamlit_feedback import streamlit_feedback
from utils import get_expression_chain, get_retriever
from langchain_core.tracers.context import collect_runs
from dotenv import load_dotenv
import os
load_dotenv()
GROQ_API_KEY = os.getenv('GROQ_API_KEY')
HF_API_KEY = os.getenv("HF_API_KEY")
COHERE_API_KEY = os.getenv("COHERE_API_KEY")
LANGSMITH_TRACING="true"
LANGSMITH_ENDPOINT="https://api.smith.langchain.com"
LANGSMITH_API_KEY=os.getenv("LANGSMITH_API_KEY")
LANGSMITH_PROJECT="pr-smug-rancher-51"
client = Client()
st.set_page_config(page_title = "MEDICAL CHATBOT")
st.subheader(f"Hello! How can I assist you today!")
memory = lc_memory.ConversationBufferMemory(
chat_memory=lc_memory.StreamlitChatMessageHistory(key="langchain_messages"),
return_messages=True,
memory_key="chat_history",
)
st.sidebar.markdown("## Feedback Scale")
feedback_option = (
"thumbs" if st.sidebar.toggle(label="`Faces` ⇄ `Thumbs`", value=False) else "faces"
)
with st.sidebar:
model_name = st.selectbox("**Model**", options=["llama-3.1-70b-versatile","gemma2-9b-it","gemma-7b-it","llama-3.2-3b-preview", "llama3-70b-8192", "mixtral-8x7b-32768"])
temp = st.slider("**Temperature**", min_value=0.0, max_value=1.0, step=0.001)
n_docs = st.number_input("**Number of retrieved documents**", min_value=0, max_value=10, value=5, step=1)
if st.sidebar.button("Clear message history"):
print("Clearing message history")
memory.clear()
retriever = get_retriever(n_docs=n_docs)
chain = get_expression_chain(retriever, model_name, temp)
for msg in st.session_state.langchain_messages:
avatar = "🦜" if msg.type == "ai" else None
with st.chat_message(msg.type, avatar=avatar):
st.markdown(msg.content)
prompt = st.chat_input(placeholder="Describe your symptoms or medical questions ?")
if prompt:
with st.chat_message("user"):
st.write(prompt)
with st.chat_message("assistant", avatar="🐒"):
message_placeholder = st.empty()
full_response = ""
input_dict = {"input": prompt.lower()}
used_docs = retriever.get_relevant_documents(prompt.lower())
with collect_runs() as cb:
for chunk in chain.stream(input_dict, config={"tags": ["MEDICAL CHATBOT"]}):
full_response += chunk.content
message_placeholder.markdown(full_response + "▌")
memory.save_context(input_dict, {"output": full_response})
st.session_state.run_id = cb.traced_runs[0].id
message_placeholder.markdown(full_response)
if used_docs:
docs_content = "\n\n".join(
[
f"Doc {i+1}:\n"
f"Source: {doc.metadata['source']}\n"
f"Title: {doc.metadata['title']}\n"
f"Content: {doc.page_content}\n"
for i, doc in enumerate(used_docs)
]
)
with st.sidebar:
st.download_button(
label="Consulted Documents",
data=docs_content,
file_name="Consulted_documents.txt",
mime="text/plain",
)
if st.session_state.get("run_id"):
run_id = st.session_state.run_id
feedback = streamlit_feedback(
feedback_type=feedback_option,
optional_text_label="[Optional] Please provide an explanation",
key=f"feedback_{run_id}",
)
score_mappings = {
"thumbs": {"👍": 1, "👎": 0},
"faces": {"😀": 1, "🙂": 0.75, "😐": 0.5, "🙁": 0.25, "😞": 0},
}
scores = score_mappings[feedback_option]
if feedback:
score = scores.get(feedback["score"])
if score is not None:
feedback_type_str = f"{feedback_option} {feedback['score']}"
feedback_record = client.create_feedback(
run_id,
feedback_type_str,
score=score,
comment=feedback.get("text"),
)
st.session_state.feedback = {
"feedback_id": str(feedback_record.id),
"score": score,
}
else:
st.warning("Invalid feedback score.") |