Spaces:
Running
Running
| from fastapi import FastAPI, HTTPException | |
| import streamlit as st | |
| import pandas as pd | |
| from pydantic import BaseModel, Field, validator | |
| import numpy as np | |
| import plotly.graph_objects as go | |
| from azure_openai import converse_with_patient, create_diagnosis | |
| from memory import get_conversation, store_conversation, update_conversation | |
| import uuid | |
| class ask_question (BaseModel): | |
| user_input: str | |
| id: str | |
| app = FastAPI() | |
| def generate_expert_confidence_chart(diagnosis): | |
| """ | |
| Extracts expert confidence data from JSON and generates a multi-colored bar chart. | |
| """ | |
| # Extract expert distribution data | |
| expert_distribution = diagnosis["expert_distribution"] | |
| # Process the data into a structured format | |
| rows = [] | |
| for key, value in expert_distribution.items(): | |
| expert, attribute = key.rsplit(", ", 1) # Ensure splitting at the last comma | |
| rows.append({"Expert": expert, "Attribute": attribute, "Value": value}) | |
| # Create a DataFrame | |
| df = pd.DataFrame(rows) | |
| # Filter the DataFrame for confidence values only | |
| df_confidence = df[df["Attribute"] == "confidence"].copy() | |
| # Merge confidence values with corresponding thinking explanations | |
| df_thinking = df[df["Attribute"] == "thinking"].copy() | |
| df_confidence = df_confidence.merge(df_thinking, on="Expert", suffixes=("_confidence", "_thinking")) | |
| # Convert confidence values to numeric | |
| df_confidence["Value_confidence"] = pd.to_numeric(df_confidence["Value_confidence"]) | |
| # Define a function to map confidence scores to colors | |
| def confidence_to_color(confidence): | |
| """ | |
| Maps confidence score (0-100) to a blended color between red (0 confidence) and green (100 confidence). | |
| """ | |
| red = np.array([255, 0, 0]) | |
| green = np.array([0, 255, 0]) | |
| blend_ratio = confidence / 100 # Normalize between 0 and 1 | |
| blended_color = (1 - blend_ratio) * red + blend_ratio * green | |
| return f"rgb({int(blended_color[0])}, {int(blended_color[1])}, {int(blended_color[2])})" | |
| # Apply color mapping | |
| df_confidence["Color"] = df_confidence["Value_confidence"].apply(confidence_to_color) | |
| # Create the bar chart | |
| fig = go.Figure() | |
| # Add bars with customized colors and reduced spacing | |
| fig.add_trace(go.Bar( | |
| y=df_confidence["Expert"], | |
| x=df_confidence["Value_confidence"], | |
| text=df_confidence["Value_confidence"], | |
| hovertext=df_confidence["Value_thinking"], | |
| orientation="h", | |
| marker=dict(color=df_confidence["Color"]), | |
| width=0.3, # Reduce bar width for closer spacing | |
| textposition="inside" | |
| )) | |
| # Update layout for better visibility | |
| fig.update_layout( | |
| title="Expert Confidence in Diagnosis", | |
| xaxis_title="Confidence Score", | |
| yaxis_title="Medical Expert", | |
| yaxis=dict(tickmode="linear", dtick=1, automargin=True), | |
| height=max(400, 40 * len(df_confidence)), # Adjust height dynamically | |
| bargap=0.1 # Reduce spacing between bars | |
| ) | |
| # Update hover template | |
| fig.update_traces( | |
| hovertemplate="<b>%{y}</b><br>Confidence: %{x}%<br>Thinking: %{hovertext}" | |
| ) | |
| # Show the plot | |
| return fig | |
| # FastAPI interface routes | |
| # @app.get("/") | |
| # async def root(): | |
| # return {"message": "Welcome to the GenAI Symptom Checker"} | |
| # @app.post("/ask") | |
| # async def ask_question(question: ask_question): | |
| # try: | |
| # user_input = question.user_input | |
| # conversation_id = question.id | |
| # exists, count, conversation_obj = get_conversation(conversation_id) | |
| # if count == 6: | |
| # response = converse_with_patient(conversation_obj, user_input) | |
| # store_conversation(conversation_id, conversation_id, user_input, response) | |
| # exists, count, conversation_obj = get_conversation(conversation_id) | |
| # diagnosis = create_diagnosis(conversation_obj) | |
| # return {"response": response, "count": count, "diagnosis": diagnosis} | |
| # if count > 6: | |
| # exists, count, conversation_obj = get_conversation(conversation_id) | |
| # diagnosis_content = next((item['content'] for item in conversation_obj if item['role'] == 'diagnosis'), None) | |
| # return {"response": "You have reached the maximum number of questions", "count": count, "diagnosis": diagnosis_content} | |
| # if exists == "PASS": | |
| # response = converse_with_patient(conversation_obj, user_input) | |
| # update_conversation(conversation_id, conversation_id, user_input, response) | |
| # return {"response": response, "count": count, "diagnosis": "none"} | |
| # else: | |
| # response = converse_with_patient("",user_input) | |
| # store_conversation(conversation_id, conversation_id, user_input, response) | |
| # return {"response": response, "count": count, "diagnosis": "none"} | |
| # except Exception as e: | |
| # raise HTTPException(status_code=500, detail=str(e)) | |
| # app config | |
| st.set_page_config(page_title="virtual clinician", page_icon=":medical_symbol:") | |
| st.title("Virtual Clinician :medical_symbol:") | |
| user_id = st.text_input("Name:", key="user_id") | |
| conversation_id = user_id | |
| # Ensure user_id is defined or fallback to a default value | |
| if not user_id: | |
| st.warning("Hi, Who am I speaking with?") | |
| else: | |
| # session state | |
| if "chat_history" not in st.session_state: | |
| st.session_state.chat_history = [ | |
| {"role": "AI", "content": f"Hello, {user_id} I am the virtual clinician. How can I help you today?"}, | |
| ] | |
| # conversation | |
| for message in st.session_state.chat_history: | |
| if message["role"] == "AI": | |
| with st.chat_message("AI"): | |
| st.write(message["content"]) | |
| elif message["role"] == "Human": | |
| with st.chat_message("Human"): | |
| st.write(message["content"]) | |
| # user input | |
| user_input = st.chat_input("Type your message here...") | |
| if user_input is not None and user_input != "": | |
| st.session_state.chat_history.append({"role": "Human", "content": user_input}) | |
| with st.chat_message("Human"): | |
| st.markdown(user_input) | |
| # this functions checks to see if the conversation exists | |
| exists, count, conversation_obj = get_conversation(conversation_id) | |
| # if the conversation does not exist, it creates a new conversation | |
| if count > 5: | |
| response = converse_with_patient(conversation_obj, user_input) | |
| conversation_obj = update_conversation(conversation_id, user_input, response) | |
| print(conversation_obj) | |
| with st.spinner("Creating a diagnosis..."): | |
| outcome, diagnosis = create_diagnosis(conversation_obj) | |
| if outcome == "SUCCESS": | |
| st.subheader("Diagnosis Summary") | |
| st.write(f"**Consensus Confidence:** {diagnosis['concensus_confidence']}%") | |
| st.write(f"**Consensus Thinking:** {diagnosis['concensus_thinking']}") | |
| st.write(f"**Evaluation Confidence:** {diagnosis['evaluate_confidence']}%") | |
| st.write(f"**Evaluation Explanation:** {diagnosis['evaluate_explanation']}") | |
| st.write(f"**Next Best Action:** {diagnosis['next_best_action_']}") | |
| st.write(f"**Next Best Action Explanation:** {diagnosis['next_best_action_explanation']}") | |
| st.write(f"**Next Best Action Confidence:** {diagnosis['next_best_action_confidence']}%") | |
| # Generate and display the plotly chart | |
| st.subheader("Expert Confidence Levels") | |
| fig = generate_expert_confidence_chart(diagnosis) | |
| st.plotly_chart(fig) | |
| # if the diagnosis is not successful, display a message | |
| if outcome == "FAIL1": | |
| st.write("Diagnosis not available Failed to find concensus") | |
| st.subheader("Incomplete Diagnosis") | |
| st.write(f"**Consensus Confidence:** {diagnosis['concensus_confidence']}%") | |
| st.write(f"**Consensus Thinking:** {diagnosis['concensus_thinking']}") | |
| st.write(f"**Next Best Action:** See GP") | |
| st.write(f"**Next Best Action Explanation:** Please give more details to help the AI better understand your symptoms ") | |
| # Generate and display the plotly chart | |
| st.subheader("Expert Confidence Levels") | |
| fig = generate_expert_confidence_chart(diagnosis) | |
| st.plotly_chart(fig) | |
| if outcome == "FAIL1": | |
| st.write("Diagnosis not available Failed to match described symptoms with know symptoms for AI diagnosis") | |
| st.subheader("Incomplete Diagnosis") | |
| st.write(f"**Consensus Confidence:** {diagnosis['concensus_confidence']}%") | |
| st.write(f"**Consensus Thinking:** {diagnosis['concensus_thinking']}") | |
| st.write(f"**Evaluation Confidence:** {diagnosis['evaluate_confidence']}%") | |
| st.write(f"**Evaluation Explanation:** {diagnosis['evaluate_explanation']}") | |
| st.write(f"**Next Best Action:** See GP") | |
| st.write(f"**Next Best Action Explanation:** Please give more details to help the AI better understand your symptoms ") | |
| # Generate and display the plotly chart | |
| st.subheader("Expert Confidence Levels") | |
| fig = generate_expert_confidence_chart(diagnosis) | |
| st.plotly_chart(fig) | |
| if exists == "PASS": | |
| response = converse_with_patient(conversation_obj, user_input) | |
| update_conversation(conversation_id, user_input, response) | |
| st.session_state.chat_history.append({"role": "AI", "content": response}) | |
| with st.chat_message("AI"): | |
| st.write(response) | |
| else: | |
| response = converse_with_patient("",user_input) | |
| store_conversation(conversation_id, user_input, response) | |
| st.session_state.chat_history.append({"role": "AI", "content": response}) | |
| with st.chat_message("AI"): | |
| st.write(response) | |
| # if exists == "FAIL": | |
| # response = converse_with_patient("",user_query) | |
| # store_conversation(conversation_id, user_query, response) | |
| # st.session_state.chat_history.append({"role": "AI", "content": response}) | |
| # # if the conversation exists use it to inform the AI's context | |
| # response = converse_with_patient(st.session_state.chat_history, user_query) | |
| # # update the conversation with the new response | |
| # update_conversation(conversation_id, user_query, response) | |
| # once 6 interactions have been made, the AI will generate a diagnosis | |
| # if count > 6: | |
| # # write last question to the chat log | |
| # st.session_state.chat_history.append({"role": "Human", "content": user_query}) | |
| # # get an AI response | |
| # response_data = converse_with_patient(st.session_state.chat_history, user_query) | |
| # # write AI response to the chat | |
| # st.session_state.chat_history.append({"role": "AI", "content": response_data}) | |
| # # send conversation to the AI to generate a diagnosis | |
| # outcome, diagnosis = create_diagnosis(conversation_obj) | |
| # # if the diagnosis is successful, display the diagnosis data | |
| # if outcome == "PASS": | |
| # st.subheader("Diagnosis Summary") | |
| # st.write(f"**Consensus Confidence:** {['concensus_confidence']}%") | |
| # st.write(f"**Consensus Thinking:** {diagnosis['concensus_thinking']}") | |
| # st.write(f"**Evaluation Confidence:** {diagnosis['evaluate_confidence']}%") | |
| # st.write(f"**Evaluation Explanation:** {diagnosis['evaluate_explanation']}") | |
| # st.write(f"**Next Best Action:** {diagnosis['next_best_action_']}") | |
| # st.write(f"**Next Best Action Explanation:** {diagnosis['next_best_action_explanation']}") | |
| # st.write(f"**Next Best Action Confidence:** {diagnosis['next_best_action_confidence']}%") | |
| # # Generate and display the plotly chart | |
| # st.subheader("Expert Confidence Levels") | |
| # fig = generate_expert_confidence_chart(diagnosis) | |
| # st.plotly_chart(fig) | |
| # # if the diagnosis is not successful, display a message | |
| # else: | |
| # st.write("Diagnosis not available") | |
| # from fastapi import FastAPI, HTTPException | |
| # import streamlit as st | |
| # import pandas as pd | |
| # from pydantic import BaseModel | |
| # import numpy as np | |
| # import plotly.graph_objects as go | |
| # from azure_openai import converse_with_patient, create_diagnosis | |
| # from memory import get_conversation, store_conversation, update_conversation, retrieve_conversation | |
| # class AskQuestion(BaseModel): | |
| # user_input: str | |
| # id: str | |
| # app = FastAPI() | |
| # def generate_expert_confidence_chart(diagnosis): | |
| # """ | |
| # Extracts expert confidence data from JSON and generates a multi-colored bar chart. | |
| # """ | |
| # expert_distribution = diagnosis.get("expert_distribution", {}) | |
| # rows = [] | |
| # for key, value in expert_distribution.items(): | |
| # expert, attribute = key.rsplit(", ", 1) | |
| # rows.append({"Expert": expert, "Attribute": attribute, "Value": value}) | |
| # df = pd.DataFrame(rows) | |
| # df_confidence = df[df["Attribute"] == "confidence"].copy() | |
| # df_thinking = df[df["Attribute"] == "thinking"].copy() | |
| # df_confidence = df_confidence.merge(df_thinking, on="Expert", suffixes=("_confidence", "_thinking")) | |
| # df_confidence["Value_confidence"] = pd.to_numeric(df_confidence["Value_confidence"]) | |
| # def confidence_to_color(confidence): | |
| # red = np.array([255, 0, 0]) | |
| # green = np.array([0, 255, 0]) | |
| # blend_ratio = confidence / 100 | |
| # blended_color = (1 - blend_ratio) * red + blend_ratio * green | |
| # return f"rgb({int(blended_color[0])}, {int(blended_color[1])}, {int(blended_color[2])})" | |
| # df_confidence["Color"] = df_confidence["Value_confidence"].apply(confidence_to_color) | |
| # fig = go.Figure() | |
| # fig.add_trace(go.Bar( | |
| # y=df_confidence["Expert"], | |
| # x=df_confidence["Value_confidence"], | |
| # text=df_confidence["Value_confidence"], | |
| # hovertext=df_confidence["Value_thinking"], | |
| # orientation="h", | |
| # marker=dict(color=df_confidence["Color"]), | |
| # width=0.3, | |
| # textposition="inside" | |
| # )) | |
| # fig.update_layout( | |
| # title="Expert Confidence in Diagnosis", | |
| # xaxis_title="Confidence Score", | |
| # yaxis_title="Medical Expert", | |
| # yaxis=dict(tickmode="linear", dtick=1, automargin=True), | |
| # height=max(400, 40 * len(df_confidence)), | |
| # bargap=0.1 | |
| # ) | |
| # fig.update_traces( | |
| # hovertemplate="<b>%{y}</b><br>Confidence: %{x}%<br>Thinking: %{hovertext}" | |
| # ) | |
| # return fig | |
| # conversation_id = "111a1" | |
| # st.set_page_config(page_title="Virtual Clinician", page_icon="🤖") | |
| # st.title("Virtual Clinician :toolbox:") | |
| # if "chat_history" not in st.session_state: | |
| # st.session_state.chat_history = [ | |
| # {"role": "AI", "content": "Hello, I am the virtual clinician. How can I help you today?"}, | |
| # ] | |
| # for message in st.session_state.chat_history: | |
| # with st.chat_message(message["role"]): | |
| # st.write(message["content"]) | |
| # user_query = st.chat_input("Type your message here...") | |
| # if user_query: | |
| # st.session_state.chat_history.append({"role": "Human", "content": user_query}) | |
| # with st.chat_message("Human"): | |
| # st.markdown(user_query) | |
| # exists, count, conversation_obj = get_conversation(conversation_id) | |
| # if exists == "FAIL": | |
| # response_data = converse_with_patient("", user_query) | |
| # st.session_state.chat_history.append({"role": "AI", "content": response_data}) | |
| # store_conversation(conversation_id, user_query, response_data) | |
| # else: | |
| # response_data = converse_with_patient(st.session_state.chat_history, user_query) | |
| # st.session_state.chat_history.append({"role": "AI", "content": response_data}) | |
| # update_conversation(conversation_id, conversation_id, user_query, response_data) | |
| # if count >= 6: | |
| # st.session_state.chat_history.append({"role": "Human", "content": user_query}) | |
| # response_data = converse_with_patient(st.session_state.chat_history, user_query) | |
| # st.session_state.chat_history.append({"role": "AI", "content": response_data}) | |
| # outcome, diagnosis = create_diagnosis(conversation_obj) | |
| # if outcome == "PASS": | |
| # st.subheader("Diagnosis Summary") | |
| # st.write(f"**Consensus Confidence:** {diagnosis['concensus_confidence']}%") | |
| # st.write(f"**Consensus Thinking:** {diagnosis['concensus_thinking']}") | |
| # st.write(f"**Evaluation Confidence:** {diagnosis['evaluate_confidence']}%") | |
| # st.write(f"**Evaluation Explanation:** {diagnosis['evaluate_explanation']}") | |
| # st.write(f"**Next Best Action:** {diagnosis['next_best_action_']}") | |
| # st.write(f"**Next Best Action Explanation:** {diagnosis['next_best_action_explanation']}") | |
| # st.write(f"**Next Best Action Confidence:** {diagnosis['next_best_action_confidence']}%") | |
| # st.subheader("Expert Confidence Levels") | |
| # fig = generate_expert_confidence_chart(diagnosis) | |
| # st.plotly_chart(fig) | |
| # else: | |
| # st.write("Diagnosis not available") | |
| # st.session_state.chat_history.append({"role": "AI", "content": response_data}) | |
| # with st.chat_message("AI"): | |
| # st.write(response_data) | |
| # from fastapi import FastAPI, HTTPException | |
| # import streamlit as st | |
| # import pandas as pd | |
| # from pydantic import BaseModel | |
| # import numpy as np | |
| # import plotly.graph_objects as go | |
| # from azure_openai import converse_with_patient, create_diagnosis | |
| # from memory import get_conversation, store_conversation, update_conversation, retrieve_conversation | |
| # class AskQuestion(BaseModel): | |
| # user_input: str | |
| # id: str | |
| # app = FastAPI() | |
| # def generate_expert_confidence_chart(diagnosis): | |
| # """ | |
| # Extracts expert confidence data from JSON and generates a multi-colored bar chart. | |
| # """ | |
| # expert_distribution = diagnosis.get("expert_distribution", {}) | |
| # rows = [] | |
| # for key, value in expert_distribution.items(): | |
| # expert, attribute = key.rsplit(", ", 1) | |
| # rows.append({"Expert": expert, "Attribute": attribute, "Value": value}) | |
| # df = pd.DataFrame(rows) | |
| # df_confidence = df[df["Attribute"] == "confidence"].copy() | |
| # df_thinking = df[df["Attribute"] == "thinking"].copy() | |
| # df_confidence = df_confidence.merge(df_thinking, on="Expert", suffixes=("_confidence", "_thinking")) | |
| # df_confidence["Value_confidence"] = pd.to_numeric(df_confidence["Value_confidence"]) | |
| # def confidence_to_color(confidence): | |
| # red = np.array([255, 0, 0]) | |
| # green = np.array([0, 255, 0]) | |
| # blend_ratio = confidence / 100 | |
| # blended_color = (1 - blend_ratio) * red + blend_ratio * green | |
| # return f"rgb({int(blended_color[0])}, {int(blended_color[1])}, {int(blended_color[2])})" | |
| # df_confidence["Color"] = df_confidence["Value_confidence"].apply(confidence_to_color) | |
| # fig = go.Figure() | |
| # fig.add_trace(go.Bar( | |
| # y=df_confidence["Expert"], | |
| # x=df_confidence["Value_confidence"], | |
| # text=df_confidence["Value_confidence"], | |
| # hovertext=df_confidence["Value_thinking"], | |
| # orientation="h", | |
| # marker=dict(color=df_confidence["Color"]), | |
| # width=0.3, | |
| # textposition="inside" | |
| # )) | |
| # fig.update_layout( | |
| # title="Expert Confidence in Diagnosis", | |
| # xaxis_title="Confidence Score", | |
| # yaxis_title="Medical Expert", | |
| # yaxis=dict(tickmode="linear", dtick=1, automargin=True), | |
| # height=max(400, 40 * len(df_confidence)), | |
| # bargap=0.1 | |
| # ) | |
| # fig.update_traces( | |
| # hovertemplate="<b>%{y}</b><br>Confidence: %{x}%<br>Thinking: %{hovertext}" | |
| # ) | |
| # return fig | |
| # conversation_id = "111a1" | |
| # st.set_page_config(page_title="Virtual Clinician", page_icon="🤖") | |
| # st.title("Virtual Clinician :toolbox:") | |
| # if "chat_history" not in st.session_state: | |
| # st.session_state.chat_history = get_conversation(conversation_id)[2] or [ | |
| # {"role": "AI", "content": "Hello, I am the virtual clinician. How can I help you today?"}, | |
| # ] | |
| # for message in st.session_state.chat_history: | |
| # with st.chat_message(message["role"]): | |
| # st.write(message["content"]) | |
| # user_query = st.chat_input("Type your message here...") | |
| # if user_query: | |
| # st.session_state.chat_history.append({"role": "Human", "content": user_query}) | |
| # with st.chat_message("Human"): | |
| # st.markdown(user_query) | |
| # exists, count, conversation_obj = get_conversation(conversation_id) | |
| # if not exists: | |
| # response_data = converse_with_patient("", user_query) | |
| # st.session_state.chat_history.append({"role": "AI", "content": response_data}) | |
| # store_conversation(conversation_id, conversation_id, user_query, response_data) | |
| # response_data = converse_with_patient(st.session_state.chat_history, user_query) | |
| # st.session_state.chat_history.append({"role": "AI", "content": response_data}) | |
| # update_conversation(conversation_id, conversation_id, user_query, response_data) | |
| # if count >= 6: | |
| # outcome, diagnosis = create_diagnosis(conversation_obj) | |
| # if outcome == "PASS": | |
| # st.subheader("Diagnosis Summary") | |
| # st.write(f"**Consensus Confidence:** {diagnosis['concensus_confidence']}%") | |
| # st.write(f"**Consensus Thinking:** {diagnosis['concensus_thinking']}") | |
| # st.write(f"**Evaluation Confidence:** {diagnosis['evaluate_confidence']}%") | |
| # st.write(f"**Evaluation Explanation:** {diagnosis['evaluate_explanation']}") | |
| # st.write(f"**Next Best Action:** {diagnosis['next_best_action_']}") | |
| # st.write(f"**Next Best Action Explanation:** {diagnosis['next_best_action_explanation']}") | |
| # st.write(f"**Next Best Action Confidence:** {diagnosis['next_best_action_confidence']}%") | |
| # st.subheader("Expert Confidence Levels") | |
| # fig = generate_expert_confidence_chart(diagnosis) | |
| # st.plotly_chart(fig) | |
| # else: | |
| # st.write("Diagnosis not available") | |
| # with st.chat_message("AI"): | |
| # st.write(response_data) | |
| # store_conversation(conversation_id, conversation_id, "", st.session_state.chat_history) | |
| # from fastapi import FastAPI, HTTPException | |
| # import streamlit as st | |
| # import pandas as pd | |
| # from pydantic import BaseModel | |
| # import numpy as np | |
| # import plotly.graph_objects as go | |
| # from azure_openai import converse_with_patient, create_diagnosis | |
| # from memory import get_conversation, store_conversation, update_conversation, retrieve_conversation | |
| # class AskQuestion(BaseModel): | |
| # user_input: str | |
| # id: str | |
| # app = FastAPI() | |
| # def generate_expert_confidence_chart(diagnosis): | |
| # """ | |
| # Extracts expert confidence data from JSON and generates a multi-colored bar chart. | |
| # """ | |
| # expert_distribution = diagnosis.get("expert_distribution", {}) | |
| # rows = [] | |
| # for key, value in expert_distribution.items(): | |
| # expert, attribute = key.rsplit(", ", 1) | |
| # rows.append({"Expert": expert, "Attribute": attribute, "Value": value}) | |
| # df = pd.DataFrame(rows) | |
| # df_confidence = df[df["Attribute"] == "confidence"].copy() | |
| # df_thinking = df[df["Attribute"] == "thinking"].copy() | |
| # df_confidence = df_confidence.merge(df_thinking, on="Expert", suffixes=("_confidence", "_thinking")) | |
| # df_confidence["Value_confidence"] = pd.to_numeric(df_confidence["Value_confidence"]) | |
| # def confidence_to_color(confidence): | |
| # red = np.array([255, 0, 0]) | |
| # green = np.array([0, 255, 0]) | |
| # blend_ratio = confidence / 100 | |
| # blended_color = (1 - blend_ratio) * red + blend_ratio * green | |
| # return f"rgb({int(blended_color[0])}, {int(blended_color[1])}, {int(blended_color[2])})" | |
| # df_confidence["Color"] = df_confidence["Value_confidence"].apply(confidence_to_color) | |
| # fig = go.Figure() | |
| # fig.add_trace(go.Bar( | |
| # y=df_confidence["Expert"], | |
| # x=df_confidence["Value_confidence"], | |
| # text=df_confidence["Value_confidence"], | |
| # hovertext=df_confidence["Value_thinking"], | |
| # orientation="h", | |
| # marker=dict(color=df_confidence["Color"]), | |
| # width=0.3, | |
| # textposition="inside" | |
| # )) | |
| # fig.update_layout( | |
| # title="Expert Confidence in Diagnosis", | |
| # xaxis_title="Confidence Score", | |
| # yaxis_title="Medical Expert", | |
| # yaxis=dict(tickmode="linear", dtick=1, automargin=True), | |
| # height=max(400, 40 * len(df_confidence)), | |
| # bargap=0.1 | |
| # ) | |
| # fig.update_traces( | |
| # hovertemplate="<b>%{y}</b><br>Confidence: %{x}%<br>Thinking: %{hovertext}" | |
| # ) | |
| # return fig | |
| # conversation_id = "111a1" | |
| # st.set_page_config(page_title="Virtual Clinician", page_icon="🤖") | |
| # st.title("Virtual Clinician :toolbox:") | |
| # if "chat_history" not in st.session_state: | |
| # st.session_state.chat_history = get_conversation(conversation_id)[2] or [ | |
| # {"role": "AI", "content": "Hello, I am the virtual clinician. How can I help you today?"}, | |
| # ] | |
| # for message in st.session_state.chat_history: | |
| # with st.chat_message(message["role"]): | |
| # st.write(message["content"]) | |
| # user_query = st.chat_input("Type your message here...") | |
| # if user_query: | |
| # st.session_state.chat_history.append({"role": "Human", "content": user_query}) | |
| # with st.chat_message("Human"): | |
| # st.markdown(user_query) | |
| # exists, count, conversation_obj = get_conversation(conversation_id) | |
| # if not exists: | |
| # response = converse_with_patient("", user_query) | |
| # store_conversation(conversation_id, conversation_id, user_query, response) | |
| # exists, count, conversation_obj = get_conversation(conversation_id) | |
| # response_data = converse_with_patient(st.session_state.chat_history, user_query) | |
| # st.session_state.chat_history.append({"role": "AI", "content": response_data}) | |
| # update_conversation(conversation_id, conversation_id, user_query, response_data) | |
| # if count >= 6: | |
| # outcome, diagnosis = create_diagnosis(conversation_obj) | |
| # if outcome == "PASS": | |
| # st.subheader("Diagnosis Summary") | |
| # st.write(f"**Consensus Confidence:** {diagnosis['concensus_confidence']}%") | |
| # st.write(f"**Consensus Thinking:** {diagnosis['concensus_thinking']}") | |
| # st.write(f"**Evaluation Confidence:** {diagnosis['evaluate_confidence']}%") | |
| # st.write(f"**Evaluation Explanation:** {diagnosis['evaluate_explanation']}") | |
| # st.write(f"**Next Best Action:** {diagnosis['next_best_action_']}") | |
| # st.write(f"**Next Best Action Explanation:** {diagnosis['next_best_action_explanation']}") | |
| # st.write(f"**Next Best Action Confidence:** {diagnosis['next_best_action_confidence']}%") | |
| # st.subheader("Expert Confidence Levels") | |
| # fig = generate_expert_confidence_chart(diagnosis) | |
| # st.plotly_chart(fig) | |
| # else: | |
| # st.write("Diagnosis not available") | |
| # with st.chat_message("AI"): | |
| # st.write(response_data) | |
| # store_conversation(conversation_id, conversation_id, "", st.session_state.chat_history) | |
| # conversation_id = "111a1" | |
| # st.set_page_config(page_title="Virtual Clinician", page_icon="🤖") | |
| # st.title("Virtual Clinician :toolbox:") | |
| # # Fetch conversation history and ensure it's a list of dictionaries | |
| # exists, count, conversation_obj = get_conversation(conversation_id) | |
| # if "chat_history" not in st.session_state: | |
| # if isinstance(conversation_obj, list) and all(isinstance(item, dict) for item in conversation_obj): | |
| # st.session_state.chat_history = conversation_obj | |
| # else: | |
| # st.session_state.chat_history = [ | |
| # {"role": "AI", "content": "Hello, I am the virtual clinician. How can I help you today?"}, | |
| # ] | |
| # # Ensure each message is a dictionary before accessing its keys | |
| # for message in st.session_state.chat_history: | |
| # if isinstance(message, dict) and "role" in message and "content" in message: | |
| # with st.chat_message(message["role"]): | |
| # st.write(message["content"]) | |
| # else: | |
| # st.error("Invalid message format in chat history.") | |
| # user_query = st.chat_input("Type your message here...") | |
| # if user_query: | |
| # st.session_state.chat_history.append({"role": "Human", "content": user_query}) | |
| # with st.chat_message("Human"): | |
| # st.markdown(user_query) | |
| # exists, count, conversation_obj = get_conversation(conversation_id) | |
| # if not exists: | |
| # response = converse_with_patient("", user_query) | |
| # store_conversation(conversation_id, conversation_id, user_query, response) | |
| # exists, count, conversation_obj = get_conversation(conversation_id) | |
| # response_data = converse_with_patient(st.session_state.chat_history, user_query) | |
| # st.session_state.chat_history.append({"role": "AI", "content": response_data}) | |
| # update_conversation(conversation_id, conversation_id, user_query, response_data) | |
| # if count >= 6: | |
| # outcome, diagnosis = create_diagnosis(conversation_obj) | |
| # if outcome == "PASS": | |
| # st.subheader("Diagnosis Summary") | |
| # st.write(f"**Consensus Confidence:** {diagnosis.get('concensus_confidence', 'N/A')}%") | |
| # st.write(f"**Consensus Thinking:** {diagnosis.get('concensus_thinking', 'N/A')}") | |
| # st.write(f"**Evaluation Confidence:** {diagnosis.get('evaluate_confidence', 'N/A')}%") | |
| # st.write(f"**Evaluation Explanation:** {diagnosis.get('evaluate_explanation', 'N/A')}") | |
| # st.write(f"**Next Best Action:** {diagnosis.get('next_best_action_', 'N/A')}") | |
| # st.write(f"**Next Best Action Explanation:** {diagnosis.get('next_best_action_explanation', 'N/A')}") | |
| # st.write(f"**Next Best Action Confidence:** {diagnosis.get('next_best_action_confidence', 'N/A')}%") | |
| # st.subheader("Expert Confidence Levels") | |
| # fig = generate_expert_confidence_chart(diagnosis) | |
| # st.plotly_chart(fig) | |
| # else: | |
| # st.write("Diagnosis not available") | |
| # with st.chat_message("AI"): | |
| # st.write(response_data) | |
| # store_conversation(conversation_id, conversation_id, "", st.session_state.chat_history) | |