Upload 9 files

views/cad_cid.py

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+import pandas as pd
+import plotly.express as px
+import streamlit as st
+from pathlib import Path
+from streamlit_extras.add_vertical_space import add_vertical_space
+from streamlit_extras.stylable_container import stylable_container
+
+
+st.title("Atualização de cadastro - UBS Flamengo")
+
+add_vertical_space(10)
+
+# Load the Excel file
+@st.cache_data
+def load_data():
+    file_path = Path("src/atual.xlsx")
+    df = pd.read_excel(file_path, sheet_name="ANALISE", engine='openpyxl')
+    df["ATUALIZADO"] = pd.to_datetime(df["ATUALIZADO"], errors="coerce", dayfirst=True)
+    df["Trimestre"] = df["ATUALIZADO"].dt.to_period("Q")
+    df["Mês"] = df["ATUALIZADO"].dt.to_period("M")
+    df["Semestre"] = df["ATUALIZADO"].dt.to_period("6M")
+
+    # Convert RUA column to string to ensure consistent sorting
+    df["RUA"] = df["RUA"].astype(str)
+
+    return df
+
+#df = load_data()
+uploaded_file = st.sidebar.file_uploader("Adicione o arquivo Excel", type= ".xlsx")
+if uploaded_file is not None:
+    df = load_data()
+
+
+# Custom CSS for dark theme
+st.markdown(
+    """
+<style>
+    .reportview-container {
+        background: #0e1117;
+        color: #fafafa;
+    }
+    .big-font {
+        font-size:30px !important;
+        font-weight: bold;
+        color: #fafafa;
+    }
+    .stSelectbox [data-baseweb="select"] {
+        background-color: #262730;
+    }
+    .stSelectbox [data-baseweb="select"] > div {
+        background-color: #262730;
+        color: #fafafa;
+    }
+</style>
+""",
+    unsafe_allow_html=True,
+)
+
+if uploaded_file is not None:
+    col1, col2, col3, col4 = st.columns([1, 1, 1, 3])
+    with col1:
+        rua_selecionada = st.selectbox("Selecione a Rua", sorted(df["RUA"].unique()))
+    with col2:
+        anos_disponiveis = sorted(df["ATUALIZADO"].dt.year.dropna().unique(), reverse=True)
+        anos_disponiveis.insert(0, "Total")
+        ano_selecionado = st.selectbox("Selecione o Ano", anos_disponiveis)
+    with col3:
+        granularidade = st.selectbox(
+            "Selecione a Granularidade", ["Mensal", "Trimestral", "Semestral"]
+        )
+    with col4:
+            st.write('')
+    # Filter data
+    if ano_selecionado == "Total":
+        dados_filtrados = df[df["RUA"] == rua_selecionada]
+    else:
+        dados_filtrados = df[
+            (df["RUA"] == rua_selecionada) & (df["ATUALIZADO"].dt.year == ano_selecionado)
+        ]
+
+    # Add period column based on granularity
+    if granularidade == "Mensal":
+        dados_filtrados["Período"] = dados_filtrados["ATUALIZADO"].dt.strftime("%m-%Y")
+    elif granularidade == "Trimestral":
+        dados_filtrados["Período"] = (
+            dados_filtrados["ATUALIZADO"].dt.to_period("Q").astype(str)
+        )
+    else:  # Semestral
+        dados_filtrados["Período"] = (
+            dados_filtrados["ATUALIZADO"].dt.to_period("6M").astype(str)
+        )
+
+# Function to create scatter plot
+    def create_scatter_plot(data, title):
+        fig = px.scatter(
+            data,
+            x="ATUALIZADO",
+            y="IDADE",
+            color="Período",
+            hover_data=["NOME", "ATUALIZADO", "Período"],
+            title=title,
+            color_discrete_sequence=px.colors.qualitative.Set3,
+        )
+        fig.update_layout(
+            xaxis_title="Período",
+            yaxis_title="Idade",
+            legend_title="Período",
+            plot_bgcolor="#0e1117",
+            paper_bgcolor="#0e1117",
+            font=dict(color="#fafafa"),
+        )
+        fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='#31333F', tickangle=45)
+        fig.update_yaxes(showgrid=True, gridwidth=1, gridcolor='#31333F')
+        return fig
+
+    # Main scatter plot
+    st.subheader(
+        f"Última Atualização na Rua {rua_selecionada} ({granularidade} - {ano_selecionado})"
+    )
+
+    c1, c2= st.columns([0.8, 0.2])
+
+    with c1:
+        fig = create_scatter_plot(dados_filtrados, f"Pacientes Pendentes de Atualização - {rua_selecionada}")
+        st.plotly_chart(fig, use_container_width=True)
+
+    with c2:
+        total_usuarios = len(dados_filtrados)
+        
+        st.dataframe(dados_filtrados[["NOME", "IDADE", "RUA", "NUM", "Período"]].sort_values(by="Período", ascending=True), hide_index=True)
+
+    st.write(f"Total de usuários: {total_usuarios}")
+    add_vertical_space()
+
+    st.write('-----')
+
+    add_vertical_space()
+
+    c3, c4, c5 = st.columns([2, 2, 1])
+
+    with c3:
+        st.markdown('')
+
+
+    with c4:
+        st.subheader("Análise por Faixa Etária")
+
+    with c5:
+        st.markdown('')
+
+
+    co1, co2, co3 = st.columns([2, 2, 2],)
+    with co1:
+        with stylable_container(
+                key="pueri",
+                css_styles="""
+                img {
+                    width: 460px;
+                    height: 180px;
+                    overflow: hidden;
+                    position: relative;
+                    object-fit: cover;
+                    border-radius: 14px; /* Adiciona bordas arredondadas */
+                    mask-image: linear-gradient(to bottom, rgba(0, 0, 0, 1), rgba(0, 0, 0, 0));
+                    -webkit-mask-image: linear-gradient(to bottom, rgba(0, 0, 0, 1), rgba(0, 0, 0, 0)); /* For Safari */                
+                }
+                """,
+            ):
+                st.image("src/images/pueri.jpg")
+
+    with co2:
+        with stylable_container(
+                key="prev",
+                css_styles="""
+                img {
+                    width: 460px;
+                    height: 180px;
+                    overflow: hidden;
+                    position: relative;
+                    object-fit: cover;
+                    border-radius: 14px; /* Adiciona bordas arredondadas */
+                    mask-image: linear-gradient(to bottom, rgba(0, 0, 0, 1), rgba(0, 0, 0, 0));
+                    -webkit-mask-image: linear-gradient(to bottom, rgba(0, 0, 0, 1), rgba(0, 0, 0, 0)); /* For Safari */                
+                }
+                """,
+            ):
+                st.image("src/images/prev.jpg")
+
+    with co3:
+        with stylable_container(
+                key="mamo",
+                css_styles="""
+                img {
+                    width: 460px;
+                    height: 180px;
+                    overflow: hidden;
+                    position: relative;
+                    object-fit: cover;
+                    border-radius: 14px; /* Adiciona bordas arredondadas */
+                    mask-image: linear-gradient(to bottom, rgba(0, 0, 0, 1), rgba(0, 0, 0, 0));
+                    -webkit-mask-image: linear-gradient(to bottom, rgba(0, 0, 0, 1), rgba(0, 0, 0, 0)); /* For Safari */                
+                }
+                """,
+            ):
+                st.image("src/images/mamo.jpg")
+
+
+    # Preventivo (25-64 anos)
+    preventivo = dados_filtrados[
+        (dados_filtrados["IDADE"].between(25, 64)) & (dados_filtrados["SEXO"] == "F")
+    ]
+    st.markdown("### Preventivo (25-64 anos)")
+    fig_preventivo = create_scatter_plot(preventivo, f"Preventivo - {rua_selecionada}")
+    st.plotly_chart(fig_preventivo, use_container_width=True)
+
+
+    col1, col2 = st.columns([0.4, 0.6])
+    with col1:
+        st.dataframe(
+            preventivo[["NOME", "RUA", "NUM", "ATUALIZADO", "IDADE", "Período"]].sort_values(
+                by="ATUALIZADO", ascending=False
+            ),
+            hide_index=True,
+        )
+    with col2:
+        st.markdown(f"#### Total de usuários: {len(preventivo)}")
+
+
+
+    add_vertical_space()
+
+    st.write('-----')
+
+    add_vertical_space()
+
+
+    # Mamografia (50-69 anos)
+    mamografia = dados_filtrados[
+        (dados_filtrados["IDADE"].between(50, 69)) & (dados_filtrados["SEXO"] == "F")
+    ]
+    st.markdown("### Mamografia (50-69 anos)")
+    fig_mamografia = create_scatter_plot(mamografia, f"Mamografia - {rua_selecionada}")
+    st.plotly_chart(fig_mamografia, use_container_width=True)
+
+
+    col1, col2 = st.columns([0.4, 0.6])
+    with col1:
+        st.dataframe(
+            mamografia[["NOME", "RUA", "NUM", "ATUALIZADO", "IDADE", "Período"]].sort_values(
+                by="ATUALIZADO", ascending=False
+            ),
+            hide_index=True,
+        )
+    with col2:
+        st.markdown(f"#### Total de usuários: {len(mamografia)}")
+
+
+
+    add_vertical_space()
+
+    st.write('-----')
+
+    add_vertical_space()
+
+
+    st.markdown("### Crianças 0 - 2 anos e 0 - 4 anos.")
+
+    # Crianças até 4 anos
+    criancasdois = dados_filtrados[dados_filtrados["IDADE"] <= 2]
+    # Crianças até 4 anos
+    criancasquatro = dados_filtrados[dados_filtrados["IDADE"] <= 4]
+
+    on = st.toggle("Alterne para ver as crianças até dois anos ou até 4 anos.")
+    if on:    
+        st.markdown("### Crianças até 2 anos")
+        fig_criancasdois = create_scatter_plot(criancasdois, f"Crianças até 2 anos - {rua_selecionada}")
+        st.plotly_chart(fig_criancasdois, use_container_width=True)
+        
+        col1, col2 = st.columns([0.4, 0.6])
+        with col1:
+            st.dataframe(
+                criancasdois[["NOME", "RUA", "NUM", "ATUALIZADO", "IDADE", "Período"]].sort_values(
+                    by="ATUALIZADO", ascending=False
+                ),
+                hide_index=True,
+        )
+        with col2:
+            st.markdown(f"#### Total de usuários: {len(criancasdois)}")
+    else:
+        st.markdown("### Crianças até 4 anos")
+        fig_criancasquatro = create_scatter_plot(criancasquatro, f"Crianças até 2 anos - {rua_selecionada}")
+        st.plotly_chart(fig_criancasquatro, use_container_width=True)
+
+        col1, col2 = st.columns([0.4, 0.6])
+        with col1:
+            st.dataframe(
+                criancasquatro[["NOME", "RUA", "NUM", "ATUALIZADO", "IDADE", "Período"]].sort_values(
+                    by="ATUALIZADO", ascending=False
+                ),
+                hide_index=True,
+        )
+        with col2:
+            st.markdown(f"#### Total de usuários: {len(criancasquatro)}")
+
+    st.write('-----------')
+    # Percentage of updates by period
+    st.subheader(
+        f"Porcentagem de Atualizações por {granularidade} na Rua {rua_selecionada}"
+    )
+
+    period_count = dados_filtrados["Período"].value_counts().sort_index()
+    period_percent = (period_count / len(dados_filtrados)) * 100
+
+    period_data = pd.DataFrame(
+        {
+            "Período": period_count.index,
+            "Porcentagem": period_percent.values.round(2),
+            "Atualizações": period_count.values,
+        }
+    )
+
+    st.dataframe(period_data, hide_index=True)
+
+    fig_period = px.bar(
+        period_data,
+        x="Período",
+        y="Porcentagem",
+        text="Atualizações",
+        title=f"Porcentagem de Atualizações por {granularidade}",
+        color="Porcentagem",
+        color_continuous_scale="Viridis",
+    )
+
+    fig_period.update_traces(
+        texttemplate="%{text}",
+        textposition="outside",
+        hovertemplate="%{x}: %{y:.2f}%<br>Atualizações: %{text}<extra></extra>",
+    )
+    fig_period.update_layout(
+        xaxis_title=granularidade,
+        yaxis_title="Porcentagem",
+        yaxis_tickformat=".2f",
+        plot_bgcolor="white",
+    )
+    fig_period.update_xaxes(showgrid=True, gridwidth=1, gridcolor="lightgray")
+    fig_period.update_yaxes(showgrid=True, gridwidth=1, gridcolor="lightgray")
+
+    st.plotly_chart(fig_period, use_container_width=True)
+else: 
+    st.markdown('# Insira o arquivo com os dados')

views/exame.py

@@ -0,0 +1,83 @@
+import streamlit as st
+from openai import OpenAI
+from langchain.document_loaders import UnstructuredFileLoader
+
+st.title("Extração de resultados")
+
+
+def load_document(file_path, mode="single"):
+    loader = UnstructuredFileLoader(file_path, mode=mode)
+    docs = loader.load()
+
+    if mode == "single":
+        return docs[0].page_content, []
+    elif mode == "elements":
+        return " ".join([str(doc.page_content) for doc in docs]), docs
+
+
+# Sidebar para entrada de chave API e seleção de modelo
+with st.sidebar:
+    openai_api_key = st.text_input(
+        "OpenAI API Key", key="chatbot_api_key", type="password"
+    )
+    st.markdown(
+        "[Pegue aqui sua chave OpenAI API](https://platform.openai.com/account/api-keys)"
+    )
+
+    uploaded_file = st.file_uploader(
+        "Escolha um arquivo PDF ou imagem", type=["pdf", "png", "jpg", "jpeg", "docx"]
+    )
+
+    if st.button("Limpar Conversa"):
+        st.session_state["messages"] = [
+            {"role": "assistant", "content": "How can I help you?"}
+        ]
+        st.experimental_rerun()
+
+st.title("💬 Chatbot")
+st.caption("🚀 A Streamlit chatbot powered by OpenAI")
+
+models = "asst_wXmB8syPqCxPWGtNqiuOXQtn"
+
+if "messages" not in st.session_state:
+    st.session_state["messages"] = [
+        {"role": "assistant", "content": "How can I help you?"}
+    ]
+
+for msg in st.session_state.messages:
+    st.chat_message(msg["role"]).write(msg["content"])
+
+
+if prompt := st.chat_input():
+    if not openai_api_key:
+        st.info("Insira sua chave API OpenAI para continuar.")
+        st.stop()
+
+    client = OpenAI(api_key=openai_api_key)
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    st.chat_message("user").write(prompt)
+    response = client.chat.completions.create(
+        model=models, messages=st.session_state.messages
+    )
+    msg = response.choices[0].message.content
+    st.session_state.messages.append({"role": "assistant", "content": msg})
+    st.chat_message("assistant").write(msg)
+
+
+if uploaded_file is not None:
+    data = load_document(st.file_uploader)
+    if data := st.session_state.get("uploaded_file", None) is not None and st.button(
+        "Enviar", type="primary"
+    ):
+        if data is not None:
+            st.session_state.exames({"role": "uploaded_file", "content": data})
+            resposta = client.chat.completions.create(
+                model=models, messages=st.session_state.exames
+            )
+            msg = resposta.choices[0].message.content
+            st.session_state.exames.append({"role": "assistant", "content": msg})
+            st.chat_message("assistant").write(msg)
+        else:
+            st.error("Não foi possível extrair texto do arquivo carregado.")
+    else:
+        st.warning("Por favor, carregue um arquivo antes de enviar.")

views/flamengo_ibge2022.geojson

@@ -0,0 +1,1934 @@
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+						[ -44.057972911, -19.972662214 ],
+						[ -44.057920778, -19.972662664 ],
+						[ -44.057919113, -19.972662678 ],
+						[ -44.057853973, -19.972663309 ],
+						[ -44.057759971, -19.972664219 ],
+						[ -44.057681551, -19.972663154 ],
+						[ -44.057601498, -19.972656791 ],
+						[ -44.057602802, -19.972652233 ],
+						[ -44.057642941, -19.972573122 ],
+						[ -44.057958452, -19.97165646 ],
+						[ -44.057912815, -19.971591804 ]
+					]
+				]
+			},
+			"properties" : {
+				"CD_SETOR" : "311860110080044P",
+				"AREA_KM2" : 0.025455,
+				"POP" : 493 }
+		},
+		    {
+			"type" : "Feature",
+			"geometry" : {
+				"type" : "Polygon",
+				"coordinates" : [
+					    [
+						[ -44.055992286, -19.971419998 ],
+						[ -44.055974662, -19.971389513 ],
+						[ -44.055959032, -19.971358489 ],
+						[ -44.055950225, -19.971325425 ],
+						[ -44.055945354, -19.971295661 ],
+						[ -44.055946586, -19.971259081 ],
+						[ -44.055950969, -19.971228982 ],
+						[ -44.055961175, -19.971199275 ],
+						[ -44.055975299, -19.971173153 ],
+						[ -44.055991985, -19.971149276 ],
+						[ -44.056014898, -19.971126962 ],
+						[ -44.056042091, -19.971106755 ],
+						[ -44.056071553, -19.971092678 ],
+						[ -44.056103554, -19.971081655 ],
+						[ -44.05613218, -19.971078197 ],
+						[ -44.056168753, -19.97107855 ],
+						[ -44.056214506, -19.971084135 ],
+						[ -44.056258477, -19.971098655 ],
+						[ -44.056290588, -19.971117107 ],
+						[ -44.056314736, -19.971099511 ],
+						[ -44.056355168, -19.971070048 ],
+						[ -44.05644372, -19.970989699 ],
+						[ -44.0565693, -19.970870761 ],
+						[ -44.056614572, -19.970833163 ],
+						[ -44.056674862, -19.970911118 ],
+						[ -44.057243578, -19.97158776 ],
+						[ -44.057590297, -19.97127642 ],
+						[ -44.057587205, -19.971273003 ],
+						[ -44.057591312, -19.971273003 ],
+						[ -44.057912815, -19.971591804 ],
+						[ -44.057958452, -19.97165646 ],
+						[ -44.057642941, -19.972573122 ],
+						[ -44.057602802, -19.972652233 ],
+						[ -44.057601498, -19.972656791 ],
+						[ -44.057522625, -19.972643847 ],
+						[ -44.057457583, -19.972627472 ],
+						[ -44.057386917, -19.972610133 ],
+						[ -44.057322271, -19.97258932 ],
+						[ -44.057245039, -19.972557106 ],
+						[ -44.057181816, -19.972525051 ],
+						[ -44.057118516, -19.972491604 ],
+						[ -44.057059438, -19.972456265 ],
+						[ -44.056989652, -19.97240621 ],
+						[ -44.056936478, -19.972361438 ],
+						[ -44.056870273, -19.972298393 ],
+						[ -44.056814726, -19.972239682 ],
+						[ -44.056806597, -19.97223109 ],
+						[ -44.056718178, -19.972134684 ],
+						[ -44.056707397, -19.972122775 ],
+						[ -44.056649162, -19.972058446 ],
+						[ -44.056616226, -19.972088543 ],
+						[ -44.056294725, -19.971745884 ],
+						[ -44.05611403, -19.971564595 ],
+						[ -44.056072923, -19.971524676 ],
+						[ -44.056037931, -19.97148333 ],
+						[ -44.056009807, -19.971433837 ],
+						[ -44.055992286, -19.971419998 ]
+					]
+				]
+			},
+			"properties" : {
+				"CD_SETOR" : "311860110080045P",
+				"AREA_KM2" : 0.021332,
+				"POP" : 402 }
+		}	
+	]
+}
+	

views/maps.py

@@ -0,0 +1,187 @@
+import folium
+import geopandas as gpd
+import plotly.express as px
+from branca.colormap import LinearColormap
+from folium.plugins import HeatMap
+from streamlit_folium import folium_static
+
+import streamlit as st
+from streamlit_extras.stylable_container import stylable_container
+from streamlit_extras.add_vertical_space import add_vertical_space
+import streamlit_shadcn_ui as ui
+
+with stylable_container(
+    key="banner",
+    css_styles="""
+    img {
+        width: 1800px;
+        height: 400px;
+        overflow: hidden;
+        position: relative;
+        object-fit: cover;
+        border-radius: 20px; /* Adiciona bordas arredondadas */
+        mask-image: linear-gradient(to bottom, rgba(0, 0, 0, 1), rgba(0, 0, 0, 0));
+        -webkit-mask-image: linear-gradient(to bottom, rgba(0, 0, 0, 1), rgba(0, 0, 0, 0)); /* For Safari */
+    }
+    """,
+):
+    st.image("src/images/mp.jpg")
+
+st.title("Mapas da área")
+add_vertical_space(5)
+st.markdown(""" ### :world_map: **UBS Flamengo: (IBGE 2022)** """)
+add_vertical_space(5)
+
+
+@st.cache_data
+def load_data():
+    """
+    A function that loads and reads geojson data for UBS Flamengo and converts it to the specified coordinate reference system.
+    """
+    return gpd.read_file("views\\flamengo_ibge2022.geojson").to_crs(epsg=4326)
+
+gdf = load_data()
+LATITUDE = -19.971591804
+LONGITUDE = -44.057912815
+lat = -19.96214
+long = -44.05603
+
+
+
+total_pop = gdf["POP"].sum()
+col1, col2, col3 = st.columns([1, 1, 5])
+
+with col1:
+    # st.write(f"###### População Total: {total_pop:,}")
+    map_type = st.selectbox("Tipo de mapa", ["População", "Densidade", "Heatmap"])
+
+with col2:
+    # st.write(f"###### Número de Setores Censitários: {len(gdf)}")
+    base_map = st.selectbox("Mapa base", ["Cartodb Positron", "OpenStreetMap"])
+
+with col3:
+    total_pop = gdf["POP"].sum()
+    st.write(
+        f"### 👪 População Total: {total_pop:,} habitantes, dados do Censo 2022 IBGE"
+    )
+    st.write(f"### 🗺️ Número de Setores Censitários: {len(gdf)}")
+
+add_vertical_space(5)
+add_vertical_space(5)
+
+col1, col2 = st.columns(2)
+with col1:
+    m = folium.Map(
+        location=[LATITUDE, LONGITUDE], tiles=base_map, zoom_start=15
+    )
+    dns_p = "Densidade pop. (hab/km²) UBS Flamengo - IBGE 2022"
+    if map_type in ["População", "Densidade"]:
+        if map_type == "População":
+            column = "POP"
+            caption = "Pop. residente UBS Flamengo IBGE 2022"
+        else:
+            gdf["DENSIDADE"] = gdf["POP"] / gdf["AREA_KM2"]
+            column = "DENSIDADE"
+            caption = dns_p
+        colorscale = px.colors.sequential.Viridis
+        colormap = LinearColormap(
+            colors=colorscale,
+            vmin=gdf[column].min(),
+            vmax=gdf[column].max(),
+            caption=caption,
+        )
+        folium.GeoJson(
+            gdf,
+            style_function=lambda feature: {
+                "fillColor": colormap(feature["properties"][column]),
+                "color": "black",
+                "weight": 1,
+                "fillOpacity": 0.7,
+            },
+            highlight_function=lambda feature: {
+                "fillColor": "#ffaf00",
+                "color": "green",
+                "weight": 3,
+                "fillOpacity": 0.9,
+            },
+            tooltip=folium.features.GeoJsonTooltip(
+                fields=["CD_SETOR", column, "AREA_KM2"],
+                aliases=[
+                    "Setor Censitário:",
+                    f"{caption}:",
+                    "Área (km²):",
+                ],
+                style=(
+                    "background-color: white; color: #333333;"
+                    "font-family: calibri; font-size: 12px;"
+                    "padding: 10px;"
+                ),
+            ),
+        ).add_to(m)
+        colormap.add_to(m)
+
+    elif map_type == "Heatmap":
+        heat_data = [
+            [
+                row["geometry"].centroid.y,
+                row["geometry"].centroid.x,
+                row["POP"],
+            ]
+            for idx, row in gdf.iterrows()
+        ]
+        HeatMap(heat_data).add_to(m)
+    folium.Marker(
+        [lat, long],
+        popup="UBS Flamengo",
+        tooltip="UBS Flamengo",
+        icon=folium.Icon(color="red", icon="info-sign"),
+    ).add_to(m)
+
+    STYLE_STATEMENT = (
+        "<style>.leaflet-control-layers"
+        "{ position: fixed; top: 10px; left: 50px; } </style>"
+    )
+    m.get_root().html.add_child(folium.Element(STYLE_STATEMENT))
+    folium_static(m)
+
+with col2:
+    fig = px.bar(
+        gdf,
+        x="CD_SETOR",
+        y="POP",
+        title="Distribuição da População por Setor Censitário",
+        color="POP",
+        color_continuous_scale=px.colors.sequential.Viridis,
+    )
+    st.plotly_chart(fig)
+
+    age_columns = [
+        "POP_0A4",
+        "POP_5A9",
+        "POP_10A14",
+        "POP_15A19",
+        "POP_20A24",
+        "POP_25A29",
+        "POP_30A34",
+        "POP_35A39",
+        "POP_40A44",
+        "POP_45A49",
+        "POP_50A54",
+        "POP_55A59",
+        "POP_60A64",
+        "POP_65A69",
+        "POP_70A74",
+        "POP_75A79",
+        "POP_80A84",
+        "POP_85A89",
+        "POP_90A94",
+        "POP_95A99",
+        "POP_100OUMAIS",
+    ]
+
+
+
+add_vertical_space(10)
+
+
+st.write('----')

views/mesames.py

@@ -0,0 +1,269 @@
+import pandas as pd
+import plotly.graph_objects as go
+from streamlit_option_menu import option_menu
+import streamlit as st
+from streamlit_extras.stylable_container import stylable_container
+
+
+st.title("Crescimento populacional - UBS Flamengo")
+
+with stylable_container(
+    key="banner",
+    css_styles="""
+    img {
+        width: 1800px;
+        height: 340px;
+        overflow: hidden;
+        position: relative;
+        object-fit: cover;
+        border-radius: 20px; /* Adiciona bordas arredondadas */
+        mask-image: linear-gradient(to bottom, rgba(0, 0, 0, 1), rgba(0, 0, 0, 0));
+        -webkit-mask-image: linear-gradient(to bottom, rgba(0, 0, 0, 1), rgba(0, 0, 0, 0)); /* For Safari */
+    }
+    """,
+):
+    st.image("src/images/pop.jpg")
+st.title("Crescimento populacional - UBS Flamengo")
+
+raw_data = [
+    {"Mês": "mai-21", "Usuários": 3402, "Domicílios": 1440, "Famílias": 1269},
+    {"Mês": "jun-21", "Usuários": 3503, "Domicílios": 1462, "Famílias": 1304},
+    {"Mês": "jul-21", "Usuários": 3559, "Domicílios": 1478, "Famílias": 1323},
+    {"Mês": "ago-21", "Usuários": 3592, "Domicílios": 1490, "Famílias": 1338},
+    {"Mês": "set-21", "Usuários": 3755, "Domicílios": 1573, "Famílias": 1380},
+    {"Mês": "out-21", "Usuários": 3776, "Domicílios": 1533, "Famílias": 1384},
+    {"Mês": "nov-21", "Usuários": 3839, "Domicílios": 1553, "Famílias": 1397},
+    {"Mês": "dez-21", "Usuários": 3926, "Domicílios": 1581, "Famílias": 1423},
+    {"Mês": "jan-22", "Usuários": 3951, "Domicílios": 1596, "Famílias": 1437},
+    {"Mês": "fev-22", "Usuários": 4035, "Domicílios": 1638, "Famílias": 1465},
+    {"Mês": "mar-22", "Usuários": 4131, "Domicílios": 1672, "Famílias": 1500},
+    {"Mês": "abr-22", "Usuários": 4306, "Domicílios": 1723, "Famílias": 1555},
+    {"Mês": "mai-22", "Usuários": 4553, "Domicílios": 1795, "Famílias": 1625},
+    {"Mês": "jun-22", "Usuários": 4639, "Domicílios": 1817, "Famílias": 1653},
+    {"Mês": "jul-22", "Usuários": 4719, "Domicílios": 1848, "Famílias": 1676},
+    {"Mês": "ago-22", "Usuários": 4776, "Domicílios": 1869, "Famílias": 1687},
+    {"Mês": "set-22", "Usuários": 4831, "Domicílios": 1883, "Famílias": 1699},
+    {"Mês": "out-22", "Usuários": 4871, "Domicílios": 1900, "Famílias": 1709},
+    {"Mês": "nov-22", "Usuários": 4874, "Domicílios": 1906, "Famílias": 1708},
+    {"Mês": "dez-22", "Usuários": 4920, "Domicílios": 1914, "Famílias": 1720},
+    {"Mês": "jan-23", "Usuários": 5135, "Domicílios": 2006, "Famílias": 1776},
+    {"Mês": "fev-23", "Usuários": 5396, "Domicílios": 2084, "Famílias": 1848},
+    {"Mês": "mar-23", "Usuários": 5544, "Domicílios": 2127, "Famílias": 1893},
+    {"Mês": "abr-23", "Usuários": 5546, "Domicílios": 2140, "Famílias": 1910},
+    {"Mês": "mai-23", "Usuários": 5579, "Domicílios": 2164, "Famílias": 1920},
+    {"Mês": "jun-23", "Usuários": 5642, "Domicílios": 2181, "Famílias": 1946},
+    {"Mês": "jul-23", "Usuários": 5681, "Domicílios": 2200, "Famílias": 1961},
+    {"Mês": "ago-23", "Usuários": 5728, "Domicílios": 2208, "Famílias": 1972},
+    {"Mês": "set-23", "Usuários": 5774, "Domicílios": 2228, "Famílias": 1983},
+    {"Mês": "out-23", "Usuários": 5841, "Domicílios": 2245, "Famílias": 2007},
+    {"Mês": "nov-23", "Usuários": 5891, "Domicílios": 2297, "Famílias": 2027},
+    {"Mês": "dez-23", "Usuários": 5933, "Domicílios": 2281, "Famílias": 2036},
+    {"Mês": "jan-24", "Usuários": 5982, "Domicílios": 2307, "Famílias": 2050},
+    {"Mês": "fev-24", "Usuários": 6005, "Domicílios": 2333, "Famílias": 2057},
+    {"Mês": "mar-24", "Usuários": 6020, "Domicílios": 2327, "Famílias": 2070},
+    {"Mês": "abr-24", "Usuários": 6074, "Domicílios": 2370, "Famílias": 2095},
+]
+
+df = pd.DataFrame(raw_data)
+
+@st.cache_data
+def processar_dados(dados, intervalo):
+    """
+    Process data based on the specified interval and return the aggregated data.
+    
+    Parameters:
+    - dados: List of dictionaries containing data for each month.
+    - intervalo: String indicating the interval for data aggregation.
+    
+    Returns:
+    - List of dictionaries with aggregated data based on the specified interval.
+    """
+    if intervalo == "Mensal":
+        return dados
+    agrupamentos = {"Trimestral": 3, "Semestral": 6, "Anual": 12}
+    dados_agrupados = []
+    for i in range(0, len(dados), agrupamentos[intervalo]):
+        grupo = dados[i : i + agrupamentos[intervalo]]
+        ultimo_Mês = grupo[-1]["Mês"]
+        dados_agrupados.append(
+            {
+                "Mês": ultimo_Mês,
+                "Usuários": max(d["Usuários"] for d in grupo),
+                "Domicílios": max(d["Domicílios"] for d in grupo),
+                "Famílias": max(d["Famílias"] for d in grupo),
+            }
+        )
+    return dados_agrupados
+
+@st.cache_data
+def formatar_data(Mês):
+    """
+    A function that formats the data based on the input Mês parameter.
+    
+    Parameters:
+    - Mês (str): A string containing the month and year separated by a hyphen.
+    
+    Returns:
+    - str: A formatted string in the format "month/year".
+    """
+    m, a = Mês.split("-")
+    return f"{m}/{a}"
+
+
+# Adicionando estilo personalizado
+st.markdown(
+    """
+    <style>
+    .stSelectbox [data-baseweb="select"] {
+        max-width: 300px;
+    }
+    .st-emotion-cache-16idsys p {
+        font-size: 20px;
+        font-weight: bold;
+        color: #4FCBFC;
+    }
+    </style>
+    """,
+    unsafe_allow_html=True,
+)
+
+# Criando duas colunas para os menus de opções
+col1, col2, col3 = st.columns([3, 2, 2])
+
+with col1:
+    st.dataframe(df, use_container_width=True,  height= 250, hide_index=True)
+
+with col2:
+    intervalo = option_menu(
+        "Intervalo de Tempo",
+        ["Mensal", "Trimestral", "Semestral", "Anual"],
+        icons=["calendar-month", "calendar-quarter", "calendar-half", "calendar-year"],
+        menu_icon="cast",
+        default_index=0,
+        styles={
+            "container": {"padding": "0!important", "background-color": "#262730"},
+            "icon": {"color": "#4FCBFC", "font-size": "18px"},
+            "nav-link": {
+                "font-size": "14px",
+                "text-align": "center",
+                "margin": "0px",
+                "padding": "10px",
+                "--hover-color": "#363940",
+                "color": "#FFFFFF",
+            },
+            "nav-link-selected": {"background-color": "#0083B8"},
+            "separator": {"border-color": "#4B4B4B"},
+        },
+    )
+
+with col3:
+    metrica = option_menu(
+        "Métrica",
+        ["Todos", "Usuários", "Domicílios", "Famílias"],
+        icons=["list", "person", "house", "people"],
+        menu_icon="cast",
+        default_index=0,
+        styles={
+            "container": {"padding": "0!important", "background-color": "#262730"},
+            "icon": {"color": "#4FCBFC", "font-size": "18px"},
+            "nav-link": {
+                "font-size": "14px",
+                "text-align": "center",
+                "margin": "0px",
+                "padding": "10px",
+                "--hover-color": "#363940",
+                "color": "#FFFFFF",
+            },
+            "nav-link-selected": {"background-color": "#0083B8"},
+            "separator": {"border-color": "#4B4B4B"},
+        },
+    )
+
+dados_processados = processar_dados(raw_data, intervalo)
+
+# Switch para mostrar valores nos pontos
+mostrar_valores = st.checkbox("Mostrar valores nos pontos", value=True)
+
+# Criação do gráfico
+fig = go.Figure()
+
+metricas = ["Usuários", "Domicílios", "Famílias"] if metrica == "Todos" else [metrica]
+cores = {"Usuários": "#1f77b4", "Domicílios": "#ff7f0e", "Famílias": "#2ca02c"}
+
+
+# Slider Component
+st.sidebar.header("Ajustes dos Balões")
+balloon_positions = {}
+for m in metricas:
+    balloon_positions[m] = st.sidebar.slider(
+        f"Posição do balão para {m}", min_value=-100, max_value=0, value=-40, step=5
+    )
+
+annotations = []
+
+for m in metricas:
+    x_data = [formatar_data(d["Mês"]) for d in dados_processados]
+    y_data = [d[m] for d in dados_processados]
+
+    fig.add_trace(
+        go.Scatter(
+            x=x_data,
+            y=y_data,
+            mode="lines+markers",
+            name=m,
+            line=dict(color=cores[m], width=3),
+            marker=dict(size=8, symbol="circle", line=dict(width=2, color="white")),
+        )
+    )
+
+    if mostrar_valores:
+        for i, (x, y) in enumerate(zip(x_data, y_data)):
+            annotations.append(
+                dict(
+                    x=x,
+                    y=y,
+                    xref="x",
+                    yref="y",
+                    text=f"{y:,.0f}",
+                    showarrow=True,
+                    arrowhead=7,
+                    ax=0,
+                    ay=balloon_positions[m],  # Usa a posição ajustada para cada métrica
+                    bgcolor=cores[m],
+                    opacity=0.8,
+                    bordercolor="white",
+                    borderwidth=2,
+                    borderpad=4,
+                    font=dict(color="white", size=10),
+                )
+            )
+
+fig.update_layout(
+    title={
+        "text": "Crescimento na Área de Saúde",
+        "y": 0.95,
+        "x": 0.5,
+        "xanchor": "center",
+        "yanchor": "top",
+        "font": dict(size=24, color="#4FCBFC"),
+    },
+    xaxis_title="Mês",
+    yaxis_title="Quantidade",
+    legend_title="Métricas",
+    template="plotly_dark",
+    plot_bgcolor="#262730",
+    paper_bgcolor="#262730",
+    font=dict(color="white"),
+    xaxis=dict(showgrid=True, gridcolor="#4B4B4B", tickangle=45),
+    yaxis=dict(showgrid=True, gridcolor="#4B4B4B"),
+    legend=dict(
+        bgcolor="rgba(0,0,0,0)",
+        bordercolor="rgba(0,0,0,0)",
+        font=dict(size=12, color="white"),
+    ),
+    margin=dict(l=50, r=50, t=80, b=50),
+    annotations=annotations,
+)
+
+st.plotly_chart(fig, use_container_width=True)
+

views/newchatgroq.py

@@ -0,0 +1,175 @@
+from typing import Generator
+
+import streamlit as st
+from groq import Groq
+
+
+
+st.title("ChatBot UBS Flamengo")
+
+def clear_all_session_state():
+    """
+    Clears all keys from the `st.session_state` dictionary.
+    This function iterates over all the keys in the `st.session_state` dictionary and deletes each key from the dictionary. 
+    This is useful when you want to reset the session state and start fresh.
+    Parameters:
+        None
+    Returns:
+        None
+    """
+    for key in list(st.session_state.keys()):
+        del st.session_state[key]
+        
+
+if st.sidebar.button('Limpar Estado da Sessão', type = "primary"):
+    clear_all_session_state()
+    st.experimental_rerun()
+
+col1, col2 =st.columns(2)
+with col1:    
+    st.write('')
+with col2:
+    st.markdown("### 📚  - Documentos, pesquisas e temas gerais!")
+
+# Configuração do cliente Groq
+client = Groq(
+    api_key=st.secrets["GROQ_API_KEY"],
+)
+
+# Inicialização do histórico de chat e modelo selecionado
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+if "selected_model" not in st.session_state:
+    st.session_state.selected_model = None
+
+# Definição dos modelos disponíveis
+modelos = {
+    "gemma-7b-it": {"name": "Gemma-7b-it", "tokens": 8192, "developer": "Google"},
+    "llama2-70b-4096": {"name": "llamA2-70b-chat", "tokens": 4096, "developer": "Meta"},
+    "llama3-70b-8192": {"name": "LLaMA3-70b-8192", "tokens": 8192, "developer": "Meta"},
+    "llama3-8b-8192": {"name": "LLaMA3-8b-8192", "tokens": 8192, "developer": "Meta"},
+    "mixtral-8x7b-32768": {"name": "Mixtral-8x7b-Instruct-v0.1", "tokens": 32768, "developer": "Mistral"},
+}
+
+
+
+# Layout para seleção do modelo e controle deslizante de max_tokens
+col1, col2 = st.columns(2)
+
+with col1:
+    opcao_modelo = st.selectbox(
+        "Escolha um modelo:",
+        options=list(modelos.keys()),
+        format_func=lambda x: modelos[x]["name"],
+        index=2  # Padrão para llama 70b
+    )
+
+prompt_model = """
+Você é um assistente virtual especializado, atuando em uma Unidade Básica de Saúde (UBS) que se
+chama UBS Flamengo em Contagem - Minas Gerais - Brasil.
+Sua função principal é auxiliar profissionais de saúde e administrativos em diversas tarefas,
+sempre se comunicando em português brasileiro formal.
+
+Comportamento e Comunicação
+
+Inicie cada interação perguntando o nome do usuário e use-o respeitosamente durante toda a conversa.
+Mantenha um tom profissional, cortês e empático em todas as interações.
+Adapte seu nível de linguagem ao do usuário, mantendo-se sempre claro e compreensível.
+Evite usar termos técnicos excessivos, a menos que solicitado ou apropriado ao contexto.
+
+Principais Responsabilidades
+
+Auxiliar na elaboração de relatórios médicos e administrativos.
+Interpretar e explicar notas técnicas e diretrizes de saúde.
+Sumarizar textos complexos relacionados à saúde pública.
+Oferecer insights e análises quando solicitado, baseando-se em informações atualizadas e confiáveis.
+Responder a perguntas sobre procedimentos padrão na UBS.
+
+Limitações e Ética
+
+Nunca forneça diagnósticos médicos ou substitua o aconselhamento de profissionais de saúde.
+Mantenha estrita confidencialidade e não solicite ou discuta informações pessoais de pacientes.
+Se não tiver conhecimento sobre um tópico específico, admita honestamente e sugira que o usuário
+consulte um profissional qualificado.
+Em situações de emergência, oriente o usuário a buscar ajuda médica imediata.
+
+Formato de Respostas
+
+Forneça respostas completas e estruturadas, usando marcadores ou numeração quando apropriado.
+Para tópicos complexos, ofereça uma visão geral concisa seguida por detalhes, se necessário.
+Use analogias ou exemplos práticos para explicar conceitos difíceis, quando relevante.
+
+Conhecimento e Atualizações
+
+Mantenha-se atualizado sobre as políticas de saúde pública brasileiras e procedimentos padrão em UBS.
+Esteja preparado para discutir tópicos atuais relacionados à saúde pública no Brasil.
+Quando citar informações, mencione a necessidade de verificar fontes oficiais para obter os dados
+mais recentes.
+
+"""
+
+# Detectar mudança de modelo e limpar histórico de chat se o modelo mudou
+if st.session_state.selected_model != opcao_modelo:
+    st.session_state.messages = [{"role": "system", "content": prompt_model}]
+    st.session_state.selected_model = opcao_modelo
+    
+
+
+        
+intervalo_max_tokens = modelos[opcao_modelo]["tokens"]
+
+with col2:
+    max_tokens = st.slider(
+        "Máximo de Tokens:",
+        min_value=512,
+        max_value=intervalo_max_tokens,
+        value=min(32768, intervalo_max_tokens),
+        step=512,
+        help=f"Ajuste o número máximo de tokens (palavras) para a resposta do modelo. Máximo para o modelo selecionado: {intervalo_max_tokens}"
+    )
+
+
+# Exibir mensagens do chat do histórico
+for message in st.session_state.messages:
+    if message["role"] != "system":
+        avatar = '🤖' if message["role"] == "assistant" else '👤'
+        with st.chat_message(message["role"], avatar=avatar):
+            st.markdown(message["content"])
+
+def generate_chat_responses(chat_complete) -> Generator[str, None, None]:
+    """Gera conteúdo de resposta do chat a partir da resposta da API Groq."""
+    for chunk in chat_complete:
+        if chunk.choices[0].delta.content:
+            yield chunk.choices[0].delta.content
+
+if prompt := st.chat_input("Digite seu prompt aqui..."):
+    st.session_state.messages.append({"role": "user", "content": prompt})
+
+    with st.chat_message("user", avatar='👤'):
+        st.markdown(prompt)
+
+    # Buscar resposta da API Groq
+    try:
+        chat_complete = client.chat.completions.create(
+            model=opcao_modelo,
+            messages=st.session_state.messages,
+            max_tokens=max_tokens,
+            stream=True
+        )
+
+        # Usar a função geradora com st.write_stream
+        with st.chat_message("assistant", avatar="🤖"):
+            response_placeholder = st.empty()
+            full_response = ""
+            for chunk in generate_chat_responses(chat_complete):
+                full_response += chunk
+                response_placeholder.markdown(full_response + "▌")
+            response_placeholder.markdown(full_response)
+        # Adicionar a resposta completa ao st.session_state.messages
+        st.session_state.messages.append({"role": "assistant", "content": full_response})
+
+
+    except Exception as e:
+        st.error(f"Ocorreu um erro: {e}", icon="🚨")
+        

views/pdf_chat.py

@@ -0,0 +1,7 @@
+import streamlit as st
+
+
+st.title("Converse com seu PDF")
+
+
+st.write('## Em breve!')

views/rag_med.py

@@ -0,0 +1,7 @@
+import streamlit as st
+
+
+st.title("Enciclopédia médica")
+
+
+st.write('## Em breve!')

views/rci.py

@@ -0,0 +1,632 @@
+"""
+This module contains the code for the 'rci' view of the application.
+"""
+
+import plotly.express as px
+import streamlit as st
+from groq import Groq
+from streamlit_extras.add_vertical_space import add_vertical_space
+from streamlit_extras.stylable_container import stylable_container
+from streamlit_option_menu import option_menu
+
+from data_cleaning import processar_arquivo
+
+
+
+#################################
+############ BANNER #############
+#################################
+
+with stylable_container(
+    key="banner",
+    css_styles="""
+    img {
+        width: 1800px;
+        height: 400px;
+        overflow: hidden;
+        position: relative;
+        object-fit: cover;
+        border-radius: 14px; /* Adiciona bordas arredondadas */
+        mask-image: linear-gradient(to bottom, rgba(0, 0, 0, 1), rgba(0, 0, 0, 0));
+        -webkit-mask-image: linear-gradient(to bottom, rgba(0, 0, 0, 1), rgba(0, 0, 0, 0)); /* For Safari */
+    }
+    """,
+):
+    st.image("src/images/banner.png")
+
+st.title("Análise de microárea")
+
+
+#################################
+############ BANNER #############
+#################################
+
+
+def clear_chat_history():
+    """
+    Clears the chat history and resets the initial analysis in the session state.
+    This function clears the chat history and resets the initial analysis in the session state.
+    It sets the value of "groq_chat_history" in the session state to an empty list and the value of "initial_analysis" to an empty string.
+    """
+    st.session_state["groq_chat_history"] = []
+    st.session_state["initial_analysis"] = ""
+
+
+#################################
+############ DATA ###############
+#################################
+
+uploaded_file = st.sidebar.file_uploader("Escolha um arquivo CSV", type="csv")
+if uploaded_file is not None:
+    dataframes = processar_arquivo(uploaded_file)
+    DF_DATA = dataframes.get("Data")
+    DF_HEAD = dataframes.get("Head")
+    DF_IDADE = dataframes.get("Idade")
+    DF_GENERO = dataframes.get("genero")
+    DF_COR = dataframes.get("cor")
+    DF_DEFICIENCIA = dataframes.get("deficiencia")
+    DF_DOENCAS = dataframes.get("doencas")
+    DF_ESCOLA = dataframes.get("Escola")
+    DF_TRANSGEN = dataframes.get("transgen")
+    for df in [
+        DF_IDADE,
+        DF_GENERO,
+        DF_COR,
+        DF_DEFICIENCIA,
+        DF_DOENCAS,
+        DF_ESCOLA,
+        DF_TRANSGEN,
+    ]:
+        if df is not None:
+            df["Descrição"] = df["Descrição"].str.capitalize()
+else:
+    st.sidebar.info("Adicione um arquivo .csv.")
+
+if st.sidebar.button("Limpar", type="primary"):
+    clear_chat_history()
+
+
+def gerar_resumo_df():
+    """
+    Generates a summary of the dataframes.
+    This function creates a summary of the dataframes by concatenating the
+    string representation of each dataframe.
+    The summary includes the name of the dataframe and its content.
+    Returns:
+        str: The summary of the dataframes.
+    """
+    resumo = ""
+    if DF_DATA is not None:
+        resumo += "Resumo DF_DATA:\n"
+        resumo += DF_DATA.to_string(index=False) + "\n\n"
+    if DF_HEAD is not None:
+        resumo += "Resumo DF_HEAD:\n"
+        resumo += DF_HEAD.to_string(index=False) + "\n\n"
+    if DF_IDADE is not None:
+        resumo += "Resumo DF_IDADE:\n"
+        resumo += DF_IDADE.to_string(index=False) + "\n\n"
+    if DF_GENERO is not None:
+        resumo += f"Resumo DF_GENERO:\n{DF_GENERO.to_string(index=False)}\n\n"
+    if DF_COR is not None:
+        resumo += f"Resumo DF_COR:\n{DF_COR.to_string(index=False)}\n\n"
+    if DF_DEFICIENCIA is not None:
+        resumo += f"Resumo DF_DEFICIENCIA:\n{DF_DEFICIENCIA.to_string(index=False)}\n\n"
+    if DF_DOENCAS is not None:
+        resumo += f"Resumo DF_DOENCAS:\n{DF_DOENCAS.to_string(index=False)}\n\n"
+    if DF_ESCOLA is not None:
+        resumo += f"Resumo DF_ESCOLA:\n{DF_ESCOLA.to_string(index=False)}\n\n"
+    if DF_TRANSGEN is not None:
+        resumo += f"Resumo DF_TRANSGEN:\n{DF_TRANSGEN.to_string(index=False)}\n\n"
+    return resumo
+
+
+#################################
+############ DATA ###############
+#################################
+
+
+###########################################
+############### LATERAL ##################
+###########################################
+
+st.sidebar.markdown(
+    """
+    ### Informações:
+    - Análise - Relatório de cadastro individual. 
+    - Iniciativa - Ubs Flamengo
+    - Acesso livre
+    ### Links:
+    ##
+    ##### - [PEC SUS](https://sape.pecsus.com.br/) 📝
+    ###
+    ##### - [Obsidian - Dr Guilherme](http://dr-guilhermeapolinario.com) 🌎
+    ##### - [GitHub - Dr Guilherme](http://dr-guilhermeapolinario.com) 🌎
+    """
+)
+st.markdown(
+    """
+    #### Instruções:
+    ##### - Acesse o site :orange[PEC SUS], na aba esquerda selecione consolidado, :blue[*RELATÓRIO DE CADASTRO INDIVIDUAL*]
+    ##### - Selecione a opção :orange[baixar arquivo .csv]. Após baixar o arquivo, selecione o arquivo .csv na aba ao lado, e pronto.
+    ##### - Clique no botão de expansão abaixo para iniciar o processo.
+    ##### - Utilize o chatbot com a inteligência artificial 🤖 Zé Flamengo para tirar suas dúvidas.
+    """
+)
+st.write("-----")
+
+st.markdown(
+    """
+    #### Dados de saúde Relatório de cadastro individual.
+    """
+)
+
+###########################################
+############### LATERAL ##################
+###########################################
+
+
+###########################################
+############### EXPANDERCOM GRÁFICOS ######
+###########################################
+
+
+def criar_grafico_personalizado(df, x_col, y_col, titulo):
+    """
+    A function to create a customized bar chart based on the input data, x
+    and y columns, and title.
+    Parameters:
+        df (DataFrame): The input DataFrame containing the data.
+        x_col (str): The column name for the x-axis data.
+        y_col (str): The column name for the y-axis data.
+        titulo (str): The title of the chart.
+    Returns:
+        fig: The customized bar chart figure.
+    """
+    fig = px.bar(
+        df,
+        x=x_col,
+        y=y_col,
+        title=titulo,
+        text=y_col,
+        color=x_col,
+        color_discrete_sequence=px.colors.qualitative.Bold,
+    )
+    fig.update_traces(texttemplate="%{text:.2s}", textposition="outside")
+    fig.update_layout(
+        uniformtext_minsize=8,
+        uniformtext_mode="hide",
+        xaxis_title=x_col,
+        yaxis_title=y_col,
+        title_font_size=24,
+        title_font_family="Arial",
+        title_font_color="white",
+        title_x=0.2,
+        paper_bgcolor="rgba(0,0,0,0)",
+        plot_bgcolor="rgba(0,0,0,0)",
+        xaxis_tickangle=-45,
+    )
+    return fig
+
+
+if uploaded_file is not None:
+    c1, c2 = st.columns(2)
+    with c1:
+        if DF_DATA is not None:
+            st.dataframe(DF_DATA, hide_index=True)
+        else:
+            st.write("Data não disponível")
+    with c2:
+        if DF_HEAD is not None:
+            st.dataframe(DF_HEAD, hide_index=True)
+        else:
+            st.write("Informações não disponíveis")
+
+if uploaded_file is not None:
+    with st.expander("Visualização", expanded=True):
+        selected_tab = option_menu(
+            menu_title=None,
+            options=[
+                "Faixa Etária",
+                "Gênero",
+                "Cor",
+                "Deficiência",
+                "Doenças",
+                "Escolaridade",
+                "Identidade de Gênero",
+            ],
+            icons=[
+                "person",
+                "gender-female",
+                "person-plus",
+                "person-wheelchair",
+                "capsule-pill",
+                "stars",
+                "gender-trans",
+            ],
+            menu_icon="cast",
+            default_index=0,
+            orientation="horizontal",
+            styles={
+                "container": {
+                    "padding": "0!important",
+                    "background-color": "#262730",
+                },
+                "icon": {"color": "#4FCBFC", "font-size": "18px"},
+                "nav-link": {
+                    "font-size": "14px",
+                    "text-align": "center",
+                    "margin": "0px",
+                    "padding": "10px",
+                    "--hover-color": "#363940",
+                    "color": "#FFFFFF",
+                },
+                "nav-link-selected": {"background-color": "#0083B8"},
+                "separator": {"border-color": "#4B4B4B"},
+            },
+        )
+        if selected_tab == "Faixa Etária" and DF_IDADE is not None:
+            st.subheader("Distribuição por Faixa Etária")
+            col1, col2 = st.columns(2)
+            with col1:
+                st.dataframe(DF_IDADE, hide_index=True)
+            with col2:
+                DF_IDADE["Masculino"] = DF_IDADE["Masculino"].astype(int) * -1
+                DF_IDADE["Feminino"] = DF_IDADE["Feminino"].astype(int)
+                fig_idade = px.bar(
+                    DF_IDADE,
+                    x=["Masculino", "Feminino"],
+                    y="Descrição",
+                    orientation="h",
+                    title="Pirâmide Etária",
+                    labels={"value": "População", "Descrição": "Faixa Etária"},
+                    color="Descrição",
+                    color_discrete_sequence=px.colors.qualitative.Set3,
+                )
+                fig_idade.update_layout(
+                    barmode="relative",
+                    xaxis_title="População",
+                    yaxis_title="Faixa Etária",
+                )
+                st.plotly_chart(fig_idade)
+            DF_IDADE["Masculino"] = DF_IDADE["Masculino"].abs()
+
+        elif selected_tab == "Gênero" and DF_GENERO is not None:
+            st.subheader("Distribuição por Gênero")
+            col1, col2 = st.columns(2)
+            with col1:
+                st.dataframe(DF_GENERO, hide_index=True)
+            with col2:
+                fig_genero = px.pie(
+                    DF_GENERO,
+                    names="Descrição",
+                    values="Valor",
+                    title="Distribuição por Gênero",
+                    color_discrete_sequence=px.colors.qualitative.Pastel,
+                )
+                st.plotly_chart(fig_genero)
+
+        elif selected_tab == "Cor" and DF_COR is not None:
+            st.subheader("Distribuição por Cor")
+            col1, col2 = st.columns(2)
+            with col1:
+                st.dataframe(DF_COR, hide_index=True)
+            with col2:
+                fig_cor = px.pie(
+                    DF_COR,
+                    names="Descrição",
+                    values="Valor",
+                    title="Distribuição por Cor",
+                    color_discrete_sequence=px.colors.qualitative.Vivid,
+                )
+                st.plotly_chart(fig_cor)
+
+        elif selected_tab == "Deficiência" and DF_DEFICIENCIA is not None:
+            st.subheader("Distribuição por Deficiência")
+            col1, col2 = st.columns(2)
+            with col1:
+                st.dataframe(DF_DEFICIENCIA, hide_index=True)
+            with col2:
+                fig_deficiencia = criar_grafico_personalizado(
+                    DF_DEFICIENCIA,
+                    x_col="Descrição",
+                    y_col="Valor",
+                    titulo="Distribuição por Deficiência",
+                )
+                st.plotly_chart(fig_deficiencia)
+
+        elif selected_tab == "Doenças" and DF_DOENCAS is not None:
+            st.subheader("Distribuição por Doenças")
+            col1, col2 = st.columns(2)
+            with col1:
+                st.dataframe(DF_DOENCAS, hide_index=True)
+            with col2:
+                fig_doencas = criar_grafico_personalizado(
+                    DF_DOENCAS,
+                    x_col="Descrição",
+                    y_col="Valor",
+                    titulo="Distribuição por Doenças",
+                )
+                st.plotly_chart(fig_doencas)
+
+        elif selected_tab == "Escolaridade" and DF_ESCOLA is not None:
+            st.subheader("Distribuição por Escolaridade")
+            col1, col2 = st.columns(2)
+            with col1:
+                st.dataframe(DF_ESCOLA, hide_index=True)
+            with col2:
+                fig_escola = criar_grafico_personalizado(
+                    DF_ESCOLA,
+                    x_col="Descrição",
+                    y_col="Valor",
+                    titulo="Distribuição por Escolaridade",
+                )
+                st.plotly_chart(fig_escola)
+
+        elif selected_tab == "Identidade de Gênero" and DF_TRANSGEN is not None:
+            st.subheader("Distribuição por Identidade de Gênero")
+            col1, col2 = st.columns(2)
+            with col1:
+                st.dataframe(DF_TRANSGEN, hide_index=True)
+            with col2:
+                fig_transgen = criar_grafico_personalizado(
+                    DF_TRANSGEN,
+                    x_col="Descrição",
+                    y_col="Valor",
+                    titulo="Distribuição por Identidade de Gênero",
+                )
+                st.plotly_chart(fig_transgen)
+
+
+###########################################
+############### EXPANDERCOM GRÁFICOS ######
+###########################################
+
+add_vertical_space(5)
+
+###########################################
+############### FOTO DO BOT ###############
+###########################################
+
+with stylable_container(
+    key="bot",
+    css_styles="""
+    img {
+        width: 120px;
+        height: 100px;
+        overflow: hidden;
+        position: relative;
+        object-fit: cover;
+        border-radius: 14px; /* Adiciona bordas arredondadas */
+    }
+    """,
+):
+    st.image("src/images/b.png")
+
+###########################################
+############### FOTO DO BOT ###############
+###########################################
+
+
+###########################################
+############### CHATBOT RCI ###############
+###########################################
+
+# Configuração inicial do Groq client
+client = Groq(api_key=st.secrets["GROQ_API_KEY"])
+
+
+# Função para gerar a análise inicial
+def generate_initial_analysis(resumo_rci):
+    """
+    Generates an initial analysis report based on the provided resumo_rci.
+    Parameters:
+        resumo_rci (str): The resumo_rci to be analyzed.
+    Returns:
+        str: The generated initial analysis report.
+    Raises:
+        Exception: If there is an error generating the initial analysis.
+    Examples:
+        >>> generate_initial_analysis("Resumo do RCI")
+        "Cabeçalho com informações gerais (data, cidadãos ativos, saída, mudança de território,
+        óbito)\n\nAnálise de Faixa Etária (Crianças de 0 a 2 anos, 0 a 4 anos, mulheres na faixa etária
+        de preventivo 25 a 64 anos, mulehres na faixa etária de mamomagrafia 50a 69 anos)\n\nComparação
+        de Sexo\n\nAnálise de Raça / Cor\n\nAnálise de
+        Escolaridade\n\nOrientação Sexual\n\nDeficiências\n\nSituações de Saúde Gerais\n\n\nFormate seu
+        relatório usando negrito para títulos de seções e subtítulos. Use listas com marcadores
+        quando apropriado para melhorar a legibilidade. Apresente seu relatório final dentro de tags
+        <relatorio></relatorio>."
+    """
+    try:
+        initial_analysis = client.chat.completions.create(
+            messages=[
+                {
+                    "role": "system",
+                    "content": "Você é um assistente de análise de dados de saúde. Sua tarefa é analisar os dados fornecidos e criar um relatório detalhado seguindo o modelo especificado. O relatório deve ser escrito em português.",
+                },
+                {
+                    "role": "user",
+                    "content": f"""
+                        Aqui estão os dados para análise:
+                        <resumo_rci>
+                        {resumo_rci}
+                        </resumo_rci>
+
+                        Analise cuidadosamente os dados fornecidos e crie um relatório
+                        seguindo o modelo apresentado. O relatório deve incluir as seguintes
+                        seções:
+                        1. Cabeçalho com informações gerais (data, cidadãos ativos, saída,
+                        mudança de território, óbito)
+                        2. Análise de Faixa Etária (Crianças de 0 a 2 anos, 0 a 4 anos, mulheres
+                        na faixa etária de preventivo 25 a 64 anos, mulehres na faixa etária de
+                        mamomagrafia 50a 69 anos)
+                        3. Comparação de Sexo
+                        4. Análise de Raça / Cor
+                        5. Análise de Escolaridade
+                        6. Orientação Sexual
+                        7. Deficiências
+                        8. Situações de Saúde Gerais
+
+                        Para cada seção:
+                        - Calcule os totais e percentuais relevantes
+                        - Faça comparações quando apropriado (por exemplo, entre masculino
+                        e feminino)
+                        - Destaque as 3 informações mais significativas
+
+                        Formate seu relatório usando negrito para títulos de seções e subtítulos.
+                        Use o título de Análise parcial, use listas com marcadores quando apropriado
+                        para melhorar a legibilidade.
+                        
+                    """,
+                },
+            ],
+            model="llama3-70b-8192",
+            temperature=0.2,
+            max_tokens=1500,
+        )
+        return initial_analysis.choices[0].message.content
+    except Exception as e:
+        st.error(f"Erro ao gerar a análise inicial: {e}")
+        return "Não foi possível gerar a análise inicial."
+
+
+# Carregar arquivo
+# uploaded_file = st.file_uploader("Escolha um arquivo CSV", type="csv")
+
+if uploaded_file is not None:
+    if (
+        "current_file" not in st.session_state
+        or st.session_state.current_file != uploaded_file.name
+    ):
+        st.session_state.current_file = uploaded_file.name
+        st.session_state.initial_analysis = None  # Reset da análise anterior
+        st.session_state.resumo_rci = None  # Reset do resumo
+
+    # Botão para gerar o resumo
+    if st.button("Gerar Resumo"):
+        with st.spinner("Gerando resumo..."):
+            st.session_state.resumo_rci = (
+                gerar_resumo_df()
+            )  # Gera o resumo do DataFrame
+            st.session_state.initial_analysis = generate_initial_analysis(
+                st.session_state.resumo_rci
+            )
+        st.success("Resumo gerado com sucesso!")
+
+col1, col2 = st.columns(2)
+
+with col1:
+    with st.expander("Resumo Inicial", expanded=True):
+        if "initial_analysis" in st.session_state:
+            st.write("**Panorama Geral dos Dados:**")
+            st.text_area(
+                "Análise Inicial",
+                value=st.session_state.initial_analysis,
+                height=300,
+                disabled=False,
+            )
+        else:
+            st.write("**Clique em 'Gerar Resumo' para analisar os dados.**")
+
+with col2:
+    with st.expander("Converse com o 🤖 Zé Flamengo", expanded=True):
+        # Inicialização do histórico do chat
+        if "groq_chat_history" not in st.session_state:
+            st.session_state.groq_chat_history = []
+
+        # Exibição do histórico do chat
+        for message in st.session_state.groq_chat_history:
+            with st.chat_message(message["role"]):
+                st.markdown(message["content"])
+
+        # Input do usuário e processamento da resposta
+        if user_message := st.chat_input(
+            "Digite sua pergunta sobre saúde na microárea:"
+        ):
+            # Adiciona a mensagem do usuário ao histórico
+            st.session_state.groq_chat_history.append(
+                {"role": "user", "content": user_message}
+            )
+
+            try:
+                # Usar o resumo já gerado
+                if "resumo_rci" not in st.session_state:
+                    st.warning("Por favor, gere o resumo antes de fazer perguntas.")
+                else:
+                    resumo_rci = st.session_state.resumo_rci
+
+                    # Preparação do contexto para a API
+                    context = f"""
+                    Seu nome é Zé Flamengo, você é um assistente virtual especializado em análise de dados
+                    médicos epidemiológicos. Você tem 20 anos de experiência em análise de dados de saúde de
+                    microáreas de um PSF (Programa Saúde da Família).
+                    Sua função é:
+                    1. Analisar dados de uma unidade básica de saúde.
+                    2. Os dados que irá analisar são provenientes do Relatório de Cadastro Individual, advindos do PEC SUS.
+                    3. Suas respostas devem ser sempre em português.
+                    4. Seja conciso e evite conversar sobre outros temas.
+                    5. Sempre retome o tema da conversa.
+                    6. Realize sempre os cálculos novamente para garantir que os resultados fornecidos sejam precisos e atualizados.
+                    Os dataframes que irá analisar são do Relatório de cadastro indiviual.
+                    ele foi transformados em texto aqui disponíveis:
+                    {resumo_rci}
+                    As faixas etárias recomendadas para exames preventivos são:
+                    - **Papanicolau (Preventivo):** Mulheres entre 25 e 64 anos, com frequência anual nos primeiros dois exames
+                    consecutivos com resultados normais, depois a cada três anos.
+                    - **Mamografia:** Mulheres entre 50 e 69 anos, com frequência bienal.
+                    Informações sobre o Relatório de Cadastro Individual:
+                    - **Objetivo:** Coletar dados sociodemográficos e de saúde dos indivíduos cadastrados em uma unidade básica de saúde.
+                    - **Principais Indicadores:** Idade, sexo, condições de saúde (doenças crônicas, gestantes, etc.), status de
+                    vacinação, hábitos de vida (tabagismo, alcoolismo, atividade física), entre outros.
+                    Exemplos de perguntas esperadas:
+                    - Qual a porcentagem de mulheres na faixa etária de preventivo?
+                    - Qual a porcentagem de mulheres na faixa etária de mamografia?
+                    - Quantas mulheres estão nas faixas etárias de preventivo e mamografia?
+                    - Qual a relação masculino/feminino?
+                    - Qual a porcentagem das doenças em relação à população total?
+                    - Use essas informações para responder às perguntas do usuário.
+                    Regras:
+                    1. Seja sempre cortês.
+                    2. Responda somente assuntos referentes ao resumo.
+                    3. Caso seja feita alguma pergunta a você diferente de resumos, responda: "Vamos voltar ao trabalho que interessa?"
+                    4. Responda sempre em português.
+                    5. Se não souber a resposta, responda: "Desculpe, mas não tenho esta informação."
+                    6. Nas suas respostas, não forneça os nomes dos dataframes, apenas responda às perguntas.
+                    7. Destaque os principais achados e tendências nos dados sempre que possível.
+                    8. Se aplicável, sugira possíveis ações ou recomendações baseadas nos dados analisados.
+                    """
+
+                    # Chamada à API Groq
+                    chat_rci = client.chat.completions.create(
+                        messages=[
+                            {"role": "system", "content": context},
+                            *st.session_state.groq_chat_history,
+                        ],
+                        model="llama3-70b-8192",
+                        temperature=0.3,
+                        max_tokens=2500,
+                    )
+
+                    # Processamento da resposta
+                    if chat_rci.choices and len(chat_rci.choices) > 0:
+                        response_message = chat_rci.choices[0].message.content
+                    else:
+                        response_message = (
+                            "Desculpe, não foi possível gerar uma resposta."
+                        )
+
+                    # Adição da resposta ao histórico
+                    st.session_state.groq_chat_history.append(
+                        {"role": "assistant", "content": response_message}
+                    )
+
+                    # Exibição da resposta
+                    with st.chat_message("assistant"):
+                        st.markdown(response_message)
+
+            except Exception as e:
+                st.error(f"Erro ao gerar a resposta: {e}")
+
+        # Botão para limpar o histórico do chat
+        if st.button("Limpar histórico do chat"):
+            st.session_state.groq_chat_history = []
+            st.rerun()