from openai import OpenAI import streamlit as st import streamlit.components.v1 as components import datetime, time from dataclasses import dataclass import math import base64 ## Firestore ?? import os # import sys # import inspect # currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) # parentdir = os.path.dirname(currentdir) # sys.path.append(parentdir) # ## ---------------------------------------------------------------- # ## LLM Part import openai from langchain_openai import ChatOpenAI, OpenAI, OpenAIEmbeddings import tiktoken from langchain.prompts.few_shot import FewShotPromptTemplate from langchain.prompts.prompt import PromptTemplate from operator import itemgetter from langchain.schema import StrOutputParser from langchain_core.output_parsers import StrOutputParser from langchain_core.runnables import RunnablePassthrough import langchain_community.embeddings.huggingface from langchain_community.embeddings.huggingface import HuggingFaceBgeEmbeddings from langchain_community.vectorstores import FAISS from langchain.chains import LLMChain from langchain.chains.conversation.memory import ConversationBufferWindowMemory #, ConversationBufferMemory, ConversationSummaryMemory, ConversationSummaryBufferMemory import os, dotenv from dotenv import load_dotenv load_dotenv() if not os.path.isdir("./.streamlit"): os.mkdir("./.streamlit") print('made streamlit folder') if not os.path.isfile("./.streamlit/secrets.toml"): with open("./.streamlit/secrets.toml", "w") as f: f.write(os.environ.get("STREAMLIT_SECRETS")) print('made new file') import db_firestore as db ## Load from streamlit!! os.environ["HF_TOKEN"] = os.environ.get("HF_TOKEN") or st.secrets["HF_TOKEN"] os.environ["OPENAI_API_KEY"] = os.environ.get("OPENAI_API_KEY") or st.secrets["OPENAI_API_KEY"] os.environ["FIREBASE_CREDENTIAL"] = os.environ.get("FIREBASE_CREDENTIAL") or st.secrets["FIREBASE_CREDENTIAL"] if "openai_model" not in st.session_state: st.session_state["openai_model"] = "gpt-3.5-turbo-1106" ## Hardcode indexes for now ## TODO: Move indexes to firebase indexes = """Bleeding ChestPain Dysphagia Headache ShortnessOfBreath Vomiting Weakness Weakness2""".split("\n") # if "selected_index" not in st.session_state: # st.session_state.selected_index = 3 # if "index_selectbox" not in st.session_state: # st.session_state.index_selectbox = "Headache" # index_selectbox = st.selectbox("Select index",indexes, index=int(st.session_state.selected_index)) # if index_selectbox != indexes[st.session_state.selected_index]: # st.session_state.selected_index = indexes.index(index_selectbox) # st.session_state.index_selectbox = index_selectbox # del st.session_state["store"] # del st.session_state["store2"] # del st.session_state["retriever"] # del st.session_state["retriever2"] # del st.session_state["chain"] # del st.session_state["chain2"] model_name = "bge-large-en-v1.5" model_kwargs = {"device": "cpu"} encode_kwargs = {"normalize_embeddings": True} if "embeddings" not in st.session_state: st.session_state.embeddings = HuggingFaceBgeEmbeddings( # model_name=model_name, model_kwargs = model_kwargs, encode_kwargs = encode_kwargs) embeddings = st.session_state.embeddings if "llm" not in st.session_state: st.session_state.llm = ChatOpenAI(model_name="gpt-3.5-turbo-1106", temperature=0) llm = st.session_state.llm if "llm_i" not in st.session_state: st.session_state.llm_i = OpenAI(model_name="gpt-3.5-turbo-instruct", temperature=0) llm_i = st.session_state.llm_i if "llm_gpt4" not in st.session_state: st.session_state.llm_gpt4 = ChatOpenAI(model_name="gpt-4-1106-preview", temperature=0) llm_gpt4 = st.session_state.llm_gpt4 # ## ------------------------------------------------------------------------------------------------ # ## Patient part # index_name = f"indexes/{st.session_state.index_selectbox}/QA" # if "store" not in st.session_state: # st.session_state.store = db.get_store(index_name, embeddings=embeddings) # store = st.session_state.store if "TEMPLATE" not in st.session_state: with open('templates/patient.txt', 'r') as file: TEMPLATE = file.read() st.session_state.TEMPLATE = TEMPLATE TEMPLATE = st.session_state.TEMPLATE # with st.expander("Patient Prompt"): # TEMPLATE = st.text_area("Patient Prompt", value=st.session_state.TEMPLATE) prompt = PromptTemplate( input_variables = ["question", "context"], template = st.session_state.TEMPLATE ) # if "retriever" not in st.session_state: # st.session_state.retriever = store.as_retriever(search_type="similarity", search_kwargs={"k":2}) # retriever = st.session_state.retriever def format_docs(docs): return "\n--------------------\n".join(doc.page_content for doc in docs) # if "memory" not in st.session_state: # st.session_state.memory = ConversationBufferWindowMemory( # llm=llm, memory_key="chat_history", input_key="question", # k=5, human_prefix="student", ai_prefix="patient",) # memory = st.session_state.memory # if ("chain" not in st.session_state # or # st.session_state.TEMPLATE != TEMPLATE): # st.session_state.chain = ( # { # "context": retriever | format_docs, # "question": RunnablePassthrough() # } | # LLMChain(llm=llm, prompt=prompt, memory=memory, verbose=False) # ) # chain = st.session_state.chain sp_mapper = {"human":"student","ai":"patient", "user":"student","assistant":"patient"} # ## ------------------------------------------------------------------------------------------------ # ## ------------------------------------------------------------------------------------------------ # ## Grader part # index_name = f"indexes/{st.session_state.index_selectbox}/Rubric" # if "store2" not in st.session_state: # st.session_state.store2 = db.get_store(index_name, embeddings=embeddings) # store2 = st.session_state.store2 if "TEMPLATE2" not in st.session_state: with open('templates/grader.txt', 'r') as file: TEMPLATE2 = file.read() st.session_state.TEMPLATE2 = TEMPLATE2 TEMPLATE2 = st.session_state.TEMPLATE2 # with st.expander("Grader Prompt"): # TEMPLATE2 = st.text_area("Grader Prompt", value=st.session_state.TEMPLATE2) prompt2 = PromptTemplate( input_variables = ["question", "context", "history"], template = st.session_state.TEMPLATE2 ) def get_patient_chat_history(_): return st.session_state.get("patient_chat_history") # if "retriever2" not in st.session_state: # st.session_state.retriever2 = store2.as_retriever(search_type="similarity", search_kwargs={"k":2}) # retriever2 = st.session_state.retriever2 # def format_docs(docs): # return "\n--------------------\n".join(doc.page_content for doc in docs) # fake_history = '\n'.join([(sp_mapper.get(i.type, i.type) + ": "+ i.content) for i in memory.chat_memory.messages]) # fake_history = '\n'.join([(sp_mapper.get(i['role'], i['role']) + ": "+ i['content']) for i in st.session_state.messages_1]) # st.write(fake_history) # def y(_): # return fake_history # if ("chain2" not in st.session_state # or # st.session_state.TEMPLATE2 != TEMPLATE2): # st.session_state.chain2 = ( # { # "context": retriever2 | format_docs, # "history": y, # "question": RunnablePassthrough(), # } | # # LLMChain(llm=llm_i, prompt=prompt2, verbose=False ) #| # LLMChain(llm=llm_gpt4, prompt=prompt2, verbose=False ) #| # | { # "json": itemgetter("text"), # "text": ( # LLMChain( # llm=llm, # prompt=PromptTemplate( # input_variables=["text"], # template="Interpret the following JSON of the student's grades, and do a write-up for each section.\n\n```json\n{text}\n```"), # verbose=False) # ) # } # ) # chain2 = st.session_state.chain2 # ## ------------------------------------------------------------------------------------------------ # ## ------------------------------------------------------------------------------------------------ # ## Streamlit now # # from dotenv import load_dotenv # # import os # # load_dotenv() # # key = os.environ.get("OPENAI_API_KEY") # # client = OpenAI(api_key=key) # if st.button("Clear History and Memory", type="primary"): # st.session_state.messages_1 = [] # st.session_state.messages_2 = [] # st.session_state.memory = ConversationBufferWindowMemory(llm=llm, memory_key="chat_history", input_key="question" ) # memory = st.session_state.memory # ## Testing HTML # # html_string = """ # # # # # # # # """ # # components.html(html_string, # # width=1280, # # height=640) # st.write("Timer has been removed, switch with this button") # if st.button(f"Switch to {'PATIENT' if st.session_state.active_chat==2 else 'GRADER'}"+".... Buggy button, please double click"): # st.session_state.active_chat = 3 - st.session_state.active_chat # # st.write("Currently in " + ('PATIENT' if st.session_state.active_chat==2 else 'GRADER')) # # Create two columns for the two chat interfaces # col1, col2 = st.columns(2) # # First chat interface # with col1: # st.subheader("Student LLM") # for message in st.session_state.messages_1: # with st.chat_message(message["role"]): # st.markdown(message["content"]) # # Second chat interface # with col2: # # st.write("pls dun spam this, its tons of tokens cos chat history") # st.subheader("Grader LLM") # st.write("grader takes a while to load... please be patient") # for message in st.session_state.messages_2: # with st.chat_message(message["role"]): # st.markdown(message["content"]) # # Timer and Input # # time_left = None # # if st.session_state.start_time: # # time_elapsed = datetime.datetime.now() - st.session_state.start_time # # time_left = datetime.timedelta(minutes=10) - time_elapsed # # st.write(f"Time left: {time_left}") # # if time_left is None or time_left > datetime.timedelta(0): # # # Chat 1 is active # # prompt = st.text_input("Enter your message for Chat 1:") # # active_chat = 1 # # messages = st.session_state.messages_1 # # elif time_left and time_left <= datetime.timedelta(0): # # # Chat 2 is active # # prompt = st.text_input("Enter your message for Chat 2:") # # active_chat = 2 # # messages = st.session_state.messages_2 # if st.session_state.active_chat==1: # text_prompt = st.text_input("Enter your message for PATIENT") # messages = st.session_state.messages_1 # else: # text_prompt = st.text_input("Enter your message for GRADER") # messages = st.session_state.messages_2 # from langchain.callbacks.manager import tracing_v2_enabled # from uuid import uuid4 # import os # if text_prompt: # messages.append({"role": "user", "content": text_prompt}) # with (col1 if st.session_state.active_chat == 1 else col2): # with st.chat_message("user"): # st.markdown(text_prompt) # with (col1 if st.session_state.active_chat == 1 else col2): # with st.chat_message("assistant"): # message_placeholder = st.empty() # if True: ## with tracing_v2_enabled(project_name = "streamlit"): # if st.session_state.active_chat==1: # full_response = chain.invoke(text_prompt).get("text") # else: # full_response = chain2.invoke(text_prompt).get("text").get("text") # message_placeholder.markdown(full_response) # messages.append({"role": "assistant", "content": full_response}) # st.write('fake history is:') # st.write(y("")) # st.write('done') ## ==================== if not st.session_state.get("scenario_list", None): st.session_state.scenario_list = indexes def init_patient_llm(): if "messages_1" not in st.session_state: st.session_state.messages_1 = [] ## messages 2? index_name = f"indexes/{st.session_state.scenario_list[st.session_state.selected_scenario]}/QA" if "store" not in st.session_state: st.session_state.store = db.get_store(index_name, embeddings=embeddings) if "retriever" not in st.session_state: st.session_state.retriever = st.session_state.store.as_retriever(search_type="similarity", search_kwargs={"k":2}) if "memory" not in st.session_state: st.session_state.memory = ConversationBufferWindowMemory( llm=llm, memory_key="chat_history", input_key="question", k=5, human_prefix="student", ai_prefix="patient",) if ("chain" not in st.session_state or st.session_state.TEMPLATE != TEMPLATE): st.session_state.chain = ( { "context": st.session_state.retriever | format_docs, "question": RunnablePassthrough() } | LLMChain(llm=llm, prompt=prompt, memory=st.session_state.memory, verbose=False) ) def init_grader_llm(): ## Grader index_name = f"indexes/{st.session_state.scenario_list[st.session_state.selected_scenario]}/Rubric" ## Reset time st.session_state.start_time = False if "store2" not in st.session_state: st.session_state.store2 = db.get_store(index_name, embeddings=embeddings) if "retriever2" not in st.session_state: st.session_state.retriever2 = st.session_state.store2.as_retriever(search_type="similarity", search_kwargs={"k":2}) ## Re-init history st.session_state["patient_chat_history"] = "History\n" + '\n'.join([(sp_mapper.get(i.type, i.type) + ": "+ i.content) for i in st.session_state.memory.chat_memory.messages]) if ("chain2" not in st.session_state or st.session_state.TEMPLATE2 != TEMPLATE2): st.session_state.chain2 = ( { "context": st.session_state.retriever2 | format_docs, "history": (get_patient_chat_history), "question": RunnablePassthrough(), } | # LLMChain(llm=llm_i, prompt=prompt2, verbose=False ) #| LLMChain(llm=llm_gpt4, prompt=prompt2, verbose=False ) #| | { "json": itemgetter("text"), "text": ( LLMChain( llm=llm, prompt=PromptTemplate( input_variables=["text"], template="Interpret the following JSON of the student's grades, and do a write-up for each section.\n\n```json\n{text}\n```"), verbose=False) ) } ) login_info = { "bob":"builder", "student1": "password", "admin":"admin" } def set_username(x): st.session_state.username = x def validate_username(username, password): if login_info.get(username) == password: set_username(username) else: st.warning("Wrong username or password") return None if not st.session_state.get("username"): ## ask to login st.title("Login") username = st.text_input("Username:") password = st.text_input("Password:", type="password") login_button = st.button("Login", on_click=validate_username, args=[username, password]) else: if True: ## Says hello and logout col_1, col_2 = st.columns([1,3]) col_2.title(f"Hello there, {st.session_state.username}") # Display logout button if col_1.button('Logout'): # Remove username from session state del st.session_state.username # Rerun the app to go back to the login view st.rerun() scenario_tab, dashboard_tab = st.tabs(["Training", "Dashboard"]) # st.header("head") # st.markdown("## markdown") # st.caption("caption") # st.divider() # import pandas as pd # import numpy as np # map_data = pd.DataFrame( # np.random.randn(1000, 2) / [50, 50] + [37.76, -122.4], # columns=['lat', 'lon']) # st.map(map_data) class ScenarioTabIndex: SELECT_SCENARIO = 0 PATIENT_LLM = 1 GRADER_LLM = 2 def set_scenario_tab_index(x): st.session_state.scenario_tab_index=x return None def select_scenario_and_change_tab(_): set_scenario_tab_index(ScenarioTabIndex.PATIENT_LLM) def go_to_patient_llm(): selected_scenario = st.session_state.get('selected_scenario') if selected_scenario is None or selected_scenario < 0: st.warning("Please select a scenario!") else: ## TODO: Clear state for time, LLM, Index, etc states = ["store", "store2","retriever","retriever2","chain","chain2"] for state_to_del in states: if state_to_del in st.session_state: del st.session_state[state_to_del] init_patient_llm() set_scenario_tab_index(ScenarioTabIndex.PATIENT_LLM) if not st.session_state.get("scenario_tab_index"): set_scenario_tab_index(ScenarioTabIndex.SELECT_SCENARIO) with scenario_tab: ## Check in select scenario if st.session_state.scenario_tab_index == ScenarioTabIndex.SELECT_SCENARIO: def change_scenario(scenario_index): st.session_state.selected_scenario = scenario_index if st.session_state.get("selected_scenario", None) is None: st.session_state.selected_scenario = -1 total_cols = 3 rows = list() # for _ in range(0, number_of_indexes, total_cols): # rows.extend(st.columns(total_cols)) st.header(f"Selected Scenario: {st.session_state.scenario_list[st.session_state.selected_scenario] if st.session_state.selected_scenario>=0 else 'None'}") for i, scenario in enumerate(st.session_state.scenario_list): if i % total_cols == 0: rows.extend(st.columns(total_cols)) curr_col = rows[(-total_cols + i % total_cols)] tile = curr_col.container(height=120) ## TODO: Implement highlight box if index is selected # if st.session_state.selected_scenario == i: # tile.markdown("", unsafe_allow_html=True) tile.write(":balloon:") tile.button(label=scenario, on_click=change_scenario, args=[i]) select_scenario_btn = st.button("Select Scenario", on_click=go_to_patient_llm, args=[]) elif st.session_state.scenario_tab_index == ScenarioTabIndex.PATIENT_LLM: st.header("Patient info") st.write("Pull the info here!!!") col1, col2, col3 = st.columns([1,3,1]) with col1: back_to_scenario_btn = st.button("Back to selection", on_click=set_scenario_tab_index, args=[ScenarioTabIndex.SELECT_SCENARIO]) with col3: start_timer_button = st.button("START") with col2: TIME_LIMIT = 60*10 ## to change to 10 minutes time.sleep(1) if start_timer_button: st.session_state.start_time = datetime.datetime.now() # st.session_state.time = -1 if not st.session_state.get('time') else st.session_state.get('time') st.session_state.start_time = False if not st.session_state.get('start_time') else st.session_state.start_time from streamlit.components.v1 import html html(f"""

Time left

""", ) with open("./public/char.png", "rb") as f: contents = f.read() data_url = base64.b64encode(contents).decode("utf-8") with open("./public/chars/Male_talk.gif", "rb") as f: contents = f.read() patient_url = base64.b64encode(contents).decode("utf-8") interactive_container = st.container() user_input_col ,r = st.columns([4,1]) def to_grader_llm(): init_grader_llm() set_scenario_tab_index(ScenarioTabIndex.GRADER_LLM) with r: to_grader_btn = st.button("To Grader", on_click=to_grader_llm) with user_input_col: user_inputs = st.text_input("", placeholder="Chat with the patient here!", key="user_inputs") if user_inputs: response = st.session_state.chain.invoke(user_inputs).get("text") st.session_state.patient_response = response with interactive_container: html(f"""
You: {st.session_state.get('user_inputs') or ''}

{'Patient: '+st.session_state.get('patient_response') if st.session_state.get('patient_response') else '...'}
""", height=500) elif st.session_state.scenario_tab_index == ScenarioTabIndex.GRADER_LLM: st.session_state.grader_output = "" if not st.session_state.get("grader_output") else st.session_state.grader_output def get_grades(): txt = f""" {st.session_state.diagnosis} {st.session_state.differential_1} {st.session_state.differential_2} {st.session_state.differential_3} """ response = st.session_state.chain2.invoke(txt) st.session_state.grader_output = response st.session_state.has_llm_output = bool(st.session_state.get("grader_output")) ## TODO: False for now, need check llm output! with st.expander("Your Diagnosis and Differentials", expanded=not st.session_state.has_llm_output): st.session_state.diagnosis = st.text_area("Input your case summary and **main** diagnosis:", placeholder="This is a young gentleman with significant family history of stroke, and medical history of poorly-controlled hypertension. He presents with acute onset of bitemporal headache associated with dysarthria and meningism symptoms. Important negatives include the absence of focal neurological deficits, ataxia, and recent trauma.") st.divider() st.session_state.differential_1 = st.text_input("Differential 1") st.session_state.differential_2 = st.text_input("Differential 2") st.session_state.differential_3 = st.text_input("Differential 3") with st.columns(6)[5]: send_for_grading = st.button("Get grades!", on_click=get_grades) with st.expander("Your rubrics", expanded=st.session_state.has_llm_output): if st.session_state.grader_output: st.write(st.session_state.grader_output.get("text").get("text")) # back_btn = st.button("back to LLM?", on_click=set_scenario_tab_index, args=[ScenarioTabIndex.PATIENT_LLM]) back_btn = st.button("New Scenario?", on_click=set_scenario_tab_index, args=[ScenarioTabIndex.SELECT_SCENARIO]) with dashboard_tab: import dotenv import firebase_admin, json from firebase_admin import credentials, storage, firestore import plotly.express as px import plotly.graph_objects as go import pandas as pd os.environ["FIREBASE_CREDENTIAL"] = dotenv.get_key(dotenv.find_dotenv(), "FIREBASE_CREDENTIAL") cred = credentials.Certificate(json.loads(os.environ.get("FIREBASE_CREDENTIAL"))) # Initialize Firebase (if not already initialized) if not firebase_admin._apps: firebase_admin.initialize_app(cred, {'storageBucket': 'healthhack-store.appspot.com'}) #firebase_admin.initialize_app(cred,{'storageBucket': 'healthhack-store.appspot.com'}) # connecting to firebase db_client = firestore.client() docs = db_client.collection("clinical_scores").stream() # Create a list of dictionaries from the documents data = [] for doc in docs: doc_dict = doc.to_dict() doc_dict['document_id'] = doc.id # In case you need the document ID later data.append(doc_dict) # Create a DataFrame df = pd.DataFrame(data) username = st.session_state.get("username") st.title("Dashboard") # Convert date from string to datetime if it's not already in datetime format df['date'] = pd.to_datetime(df['date'], errors='coerce') # Streamlit page configuration #st.set_page_config(page_title="Interactive Data Dashboard", layout="wide") # Use df_selection for filtering data based on authenticated user if username != 'admin': df_selection = df[df['name'] == username] else: df_selection = df # Admin sees all data # Chart Title: Student Performance Dashboard st.title(":bar_chart: Student Performance Dashboard") st.markdown("##") # Chart 1: Total attempts if df_selection.empty: st.error("No data available to display.") else: # Total attempts by name (filtered) total_attempts_by_name = df_selection.groupby("name")['date'].count().reset_index() total_attempts_by_name.columns = ['name', 'total_attempts'] # For a single point or multiple points, use a scatter plot fig_total_attempts = px.scatter( total_attempts_by_name, x="name", y="total_attempts", title="Total Attempts", size='total_attempts', # Adjust the size of points color_discrete_sequence=["#0083B8"] * len(total_attempts_by_name), template="plotly_white", text='total_attempts' # Display total_attempts as text labels ) # Add text annotation for each point for line in range(0, total_attempts_by_name.shape[0]): fig_total_attempts.add_annotation( text=str(total_attempts_by_name['total_attempts'].iloc[line]), x=total_attempts_by_name['name'].iloc[line], y=total_attempts_by_name['total_attempts'].iloc[line], showarrow=True, font=dict(family="Courier New, monospace", size=18, color="#ffffff"), align="center", arrowhead=2, arrowsize=1, arrowwidth=2, arrowcolor="#636363", ax=20, ay=-30, bordercolor="#c7c7c7", borderwidth=2, borderpad=4, bgcolor="#ff7f0e", opacity=0.8 ) # Update traces for styling fig_total_attempts.update_traces(marker=dict(size=12), selector=dict(mode='markers+text')) # Display the scatter plot in Streamlit st.plotly_chart(fig_total_attempts, use_container_width=True) # Chart 2 (students only): Personal scores over time if username != 'admin': # Sort the DataFrame by 'date' in chronological order df_selection = df_selection.sort_values(by='date') #fig = px.bar(df_selection, x='date', y='global_score', title='Your scores!') if len(df_selection) > 1: # # If more than one point, use a bar chart # fig = px.bar(df_selection, x='date', y='global_score', title='Global Score Over Time') # # fig.update_yaxes( # # tickmode='array', # # tickvals=[1, 2, 3, 4, 5], # Reverse the order of tickvals # # ticktext=['A', 'B','C','D','E'] # Reverse the order of ticktext # # ) # Mapping dictionary grade_to_score = {'A': 100, 'B': 80, 'C': 60, 'D': 40, 'E': 20} # Apply mapping to convert letter grades to numerical scores df_selection['numeric_score'] = df_selection['global_score'].map(grade_to_score) # Sort the DataFrame by 'date' in chronological order df_selection = df_selection.sort_values(by='date') # Check if there's more than one point in the DataFrame if len(df_selection) > 1: # Create a bar chart using Plotly Express fig = px.bar(df_selection, x='date', y='numeric_score', title='Your scores over time') else: # Create a bar chart with just one point fig = px.bar(df_selection, x='date', y='numeric_score', title='Global Score') # Manually set the y-axis ticks and labels fig.update_yaxes( tickmode='array', tickvals=list(grade_to_score.values()), # Positions for the ticks ticktext=list(grade_to_score.keys()), # Text labels for the ticks range=[0, 120] # Extend the range a bit beyond 100 to accommodate 'A' ) # # Use st.plotly_chart to display the chart in Streamlit # st.plotly_chart(fig, use_container_width=True) else: # For a single point, use a scatter plot fig = px.scatter(df_selection, x='date', y='global_score', title='Global Score', text='global_score', size_max=60) # Add text annotation for line in range(0,df_selection.shape[0]): fig.add_annotation(text=df_selection['global_score'].iloc[line], x=df_selection['date'].iloc[line], y=df_selection['global_score'].iloc[line], showarrow=True, font=dict(family="Courier New, monospace", size=18, color="#ffffff"), align="center", arrowhead=2, arrowsize=1, arrowwidth=2, arrowcolor="#636363", ax=20, ay=-30, bordercolor="#c7c7c7", borderwidth=2, borderpad=4, bgcolor="#ff7f0e", opacity=0.8) fig.update_traces(marker=dict(size=12), selector=dict(mode='markers+text')) # Display the chart in Streamlit st.plotly_chart(fig, use_container_width=True) # Show students their scores over time st.dataframe(df_selection[['date', 'global_score', 'name']]) # Chart 3 (admin only): Global score chart # Define the order of categories explicitly order_of_categories = ['A', 'B', 'C', 'D', 'E'] # Convert global_score to a categorical type with the specified order df_selection['global_score'] = pd.Categorical(df_selection['global_score'], categories=order_of_categories, ordered=True) # Plot the histogram fig_score_distribution = px.histogram( df_selection, x="global_score", title="Global Score Distribution", color_discrete_sequence=["#33CFA5"], category_orders={"global_score": ["A", "B", "C", "D", "E"]} ) if username == 'admin': st.plotly_chart(fig_score_distribution, use_container_width=True) # Chart 4 (admin only): Students with <5 attempts (filtered) if username == 'admin': students_with_less_than_5_attempts = total_attempts_by_name[total_attempts_by_name['total_attempts'] < 5] fig_less_than_5_attempts = px.bar( students_with_less_than_5_attempts, x="name", y="total_attempts", title="Students with <5 Attempts", color_discrete_sequence=["#D62728"] * len(students_with_less_than_5_attempts), template="plotly_white", ) if username == 'admin': st.plotly_chart(fig_less_than_5_attempts, use_container_width=True) # Selection of a student for detailed view (<5 attempts) - based on filtered data if username == 'admin': selected_student_less_than_5 = st.selectbox("Select a student with less than 5 attempts to view details:", students_with_less_than_5_attempts['name']) if selected_student_less_than_5: st.write(df_selection[df_selection['name'] == selected_student_less_than_5]) # Chart 5 (admin only): Students with at least one global score of 'C', 'D', 'E' (filtered) if username == 'admin': students_with_cde = df_selection[df_selection['global_score'].isin(['C', 'D', 'E'])].groupby("name")['date'].count().reset_index() students_with_cde.columns = ['name', 'total_attempts'] fig_students_with_cde = px.bar( students_with_cde, x="name", y="total_attempts", title="Students with at least one global score of 'C', 'D', 'E'", color_discrete_sequence=["#FF7F0E"] * len(students_with_cde), template="plotly_white", ) st.plotly_chart(fig_students_with_cde, use_container_width=True) # Selection of a student for detailed view (score of 'C', 'D', 'E') - based on filtered data if username == 'admin': selected_student_cde = st.selectbox("Select a student with at least one score of 'C', 'D', 'E' to view details:", students_with_cde['name']) if selected_student_cde: st.write(df_selection[df_selection['name'] == selected_student_cde]) # Chart 7 (all): Radar Chart # Mapping grades to numeric values grade_to_numeric = {'A': 90, 'B': 70, 'C': 50, 'D': 30, 'E': 10} df.replace(grade_to_numeric, inplace=True) # Calculate average numeric scores for each category average_scores = df.groupby('name')[['hx_PC_score', 'hx_AS_score', 'hx_others_score', 'differentials_score']].mean().reset_index() if username == 'admin': st.title('Average Scores Radar Chart') else: st.title('Performance in each segment as compared to your friends!') # Categories for the radar chart categories = ['Presenting complaint', 'Associated symptoms', '(Others)', 'Differentials'] st.markdown(""" ### Double click on the names in the legend to include/exclude them from the plot. """) # Custom colors for better contrast colors = ['gold', 'cyan', 'magenta', 'green'] # Plotly Radar Chart fig = go.Figure() for index, row in average_scores.iterrows(): fig.add_trace(go.Scatterpolar( r=[row['hx_PC_score'], row['hx_AS_score'], row['hx_others_score'], row['differentials_score']], theta=categories, fill='toself', name=row['name'], line=dict(color=colors[index % len(colors)]) )) fig.update_layout( polar=dict( radialaxis=dict( visible=True, range=[0, 100], # Numeric range tickvals=[10, 30, 50, 70, 90], # Positions for the grade labels ticktext=['E', 'D', 'C', 'B', 'A'] # Grade labels )), showlegend=True, height=600, # Set the height of the figure width=600 # Set the width of the figure ) # Display the figure in Streamlit st.plotly_chart(fig, use_container_width=True)