Update app.py

app.py

@@ -1,699 +1,237 @@
-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)
+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"**Diagnosis:** {diagnosis['concensus_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:** {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"**Diagnosis:** {diagnosis['concensus_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 == "FAIL2":
+                        st.write("Diagnosis not available Failed to match described symptoms with know symptoms  for AI diagnosis")
+                        st.subheader("Incomplete Diagnosis")
+                        st.write(f"**Diagnosis:** {diagnosis['concensus_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)
+