Merge pull request #57 from boettiger-lab/add_llms

change to docker

Author: cassiebuhler

Dockerfile

@@ -0,0 +1,12 @@
+FROM python:3.12-slim
+WORKDIR /app
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    curl \
+    git \
+    libgdal-dev \
+    && rm -rf /var/lib/apt/lists/*
+COPY requirements.txt /app/requirements.txt
+RUN pip install --no-cache-dir -r /app/requirements.txt
+COPY app/ /app/app/
+ENTRYPOINT ["bash", "-lc", "streamlit run app/app.py --server.address=0.0.0.0 --server.port ${PORT:-7860}"]

README.md

@@ -3,10 +3,17 @@ title: CA 30x30
 emoji: 🏞️
 colorFrom: green
 colorTo: yellow
-sdk: streamlit
+sdk: docker
 app_file: app/app.py
 pinned: false
 license: bsd
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# CA 30x30 Planning & Assessment Prototype
+
+This repository contains the source code for the [**CA 30x30 Planning & Assessment Prototype**](https://huggingface.co/spaces/boettiger-lab/ca-30x30) which is hosted on Hugging Face. 
+
+## Description 
+Proof of concept for a decision support tool developed in partnership with California Biodiversity Network participants through a co-design process. The tool can answer complex, real world natural language queries asked by conservation partner organizations, responding with reproducible, verifiable data summaries, charts, maps and text through careful integration of open weights language models and cloud optimized data.
+
+[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.14933818.svg)](https://doi.org/10.5281/zenodo.14933818)

app/app.py

@@ -1,7 +1,7 @@
 import streamlit as st
 import streamlit.components.v1 as components
 import base64
-import leafmap.maplibregl as leafmap
+import importlib
 import altair as alt
 import ibis
 from ibis import _
@@ -124,11 +124,12 @@
 
 st.divider()
 
-           
-m = leafmap.Map(style="positron")
-#############
-
+# initialize map
+leafmap = importlib.import_module("leafmap.maplibregl")
+m = leafmap.Map(center=(-120, 36), style="positron", zoom=5,
+                controls=controls, attribution_control=False, use_message_queue=True)
 
+#############
 
 chatbot_container = st.container()
 with chatbot_container:
@@ -193,7 +194,7 @@ class SQLResponse(BaseModel):
     'svi', 
 ]}
 
-def run_sql(query,color_choice):
+def run_sql(query, color_choice):
     """
     Filter data based on an LLM-generated SQL query and return matching IDs.
 
@@ -203,43 +204,39 @@ def run_sql(query,color_choice):
     """
     output = few_shot_structured_llm.invoke(query)
     sql_query = output.sql_query
-    explanation =output.explanation
+    explanation = output.explanation
     if not sql_query: # if the chatbot can't generate a SQL query.
         st.success(explanation)
         return pd.DataFrame({'id' : []}), [], []
 
     result = ca.sql(sql_query).execute()
-    if result.empty :
+    if result.empty:
         explanation = "This query did not return any results. Please try again with a different query."
         st.warning(explanation, icon="⚠️")
         st.caption("SQL Query:")
-        st.code(sql_query,language = "sql") 
+        st.code(sql_query, language = "sql") 
         if 'geom' in result.columns:
-            return result.drop('geom',axis = 1),  sql_query, explanation
+            return result.drop('geom', axis=1), sql_query, explanation
         else: 
             return result, sql_query, explanation
 
     elif ("id" and "geom" in result.columns): 
         style = get_pmtiles_style_llm(style_options[color_choice], result["id"].tolist())
-        legend, position, bg_color, fontsize = get_legend(style_options,color_choice)
-
-        m.add_legend(legend_dict = legend, position = position, bg_color = bg_color, fontsize = fontsize)
-        m.add_pmtiles(ca_pmtiles, style=style, opacity=alpha, tooltip=True, fit_bounds=True)
+        m.add_pmtiles(ca_pmtiles, style=style, name="30x30 Conserved Areas (Terrestrial)",
+                      attribution="CA Nature (2024)", tooltip=True)
         m.fit_bounds(result.total_bounds.tolist())    
-        result = result.drop('geom',axis = 1) #printing to streamlit so I need to drop geom
+        result = result.drop('geom', axis=1) #printing to streamlit so I need to drop geom
     else:   
         st.write(result)  # if we aren't mapping, just print out the data  
 
     with st.popover("Explanation"):
         st.write(explanation)
         st.caption("SQL Query:")
-        st.code(sql_query,language = "sql") 
+        st.code(sql_query, language="sql") 
 
     return result, sql_query, explanation
 
 
-
-
 #############
 
 filters = {}
@@ -252,7 +249,6 @@ def run_sql(query,color_choice):
         - 💬 For a more tailored experience, query our dataset of protected areas and their precomputed mean values for each of the displayed layers, using the experimental chatbot. The language model tries to answer natural language questions by drawing only from curated datasets (listed below).
         '''
 
-    
     st.divider()
     color_choice = st.radio("Group by:", style_options, key = "color", help = "Select a category to change map colors and chart groupings.")   
     colorby_vals = get_color_vals(style_options, color_choice) #get options for selected color_by column 
@@ -267,9 +263,9 @@ def run_sql(query,color_choice):
         prompt = st.chat_input(example_query, key="chain", max_chars=300)
 
 with chatbot_container:      
-        _,log_query_col, _ = st.columns([.001, 5,1], vertical_alignment = "top")
-        with log_query_col:
-            log_queries = st.checkbox("Save query", value = True, help = "Saving your queries helps improve this tool and guide conservation efforts. Your data is stored in a private location. For more details, see 'Why save your queries?' at the bottom of this page.")
+    _,log_query_col, _ = st.columns([.001, 5,1], vertical_alignment = "top")
+    with log_query_col:
+        log_queries = st.checkbox("Save query", value = True, help = "Saving your queries helps improve this tool and guide conservation efforts. Your data is stored in a private location. For more details, see 'Why save your queries?' at the bottom of this page.")
 
 
 with st.container():
@@ -279,7 +275,7 @@ def run_sql(query,color_choice):
             with st.chat_message("assistant"):
                 with st.spinner("Invoking query..."):
 
-                    out, sql_query, llm_explanation = run_sql(prompt,color_choice)
+                    out, sql_query, llm_explanation = run_sql(prompt, color_choice)
                     minio_logger(log_queries, prompt, sql_query, llm_explanation, llm_choice, 'query_log_prototype.csv', "shared-ca30x30-app")
 
                     if ("id" in out.columns) and (not out.empty):
@@ -308,52 +304,25 @@ def run_sql(query,color_choice):
         a_bio = st.slider("transparency", 0.0, 1.0, 0.1, key = "biodiversity")
         show_richness = st.toggle("Species Richness", key = "richness", value=chatbot_toggles['richness'])
         show_rsr = st.toggle("Range-Size Rarity", key = "rsr", value=chatbot_toggles['rsr'])
-        
-        if show_richness:
-            m.add_tile_layer(url_sr, name="MOBI Species Richness",opacity=a_bio)
-        if show_rsr:           
-            m.add_tile_layer(url_rsr, name="MOBI Range-Size Rarity", opacity=a_bio)
 
     #Carbon Section
     with st.expander("⛅ Carbon & Climate"):
         a_climate = st.slider("transparency", 0.0, 1.0, 0.15, key = "climate")
         show_irrecoverable_carbon = st.toggle("Irrecoverable Carbon", key = "irrecoverable_carbon", value=chatbot_toggles['irrecoverable_carbon'])
         show_manageable_carbon = st.toggle("Manageable Carbon", key = "manageable_carbon", value=chatbot_toggles['manageable_carbon'])
-        
-        if show_irrecoverable_carbon:
-            m.add_cog_layer(url_irr_carbon, palette="reds", name="Irrecoverable Carbon", opacity = a_climate, fit_bounds=False)
-        
-        if show_manageable_carbon:
-           m.add_cog_layer(url_man_carbon, palette="purples", name="Manageable Carbon", opacity = a_climate, fit_bounds=False)
-
 
     # People Section 
     with st.expander("👤 People"):
         a_people = st.slider("transparency", 0.0, 1.0, 0.1, key = "SVI")
         show_justice40 = st.toggle("Disadvantaged Communities (Justice40)", key = "disadvantaged_communities", value=chatbot_toggles['disadvantaged_communities'])
         show_sv = st.toggle("Social Vulnerability Index (SVI)", key = "svi", value=chatbot_toggles['svi'])
 
-        if show_justice40:
-            m.add_pmtiles(url_justice40, style=justice40_style, name="Justice40", opacity=a_people, tooltip=False, fit_bounds = False)
-            
-        if show_sv:
-            m.add_pmtiles(url_svi, style = svi_style, opacity=a_people, tooltip=False, fit_bounds = False)
-        
     # Fire Section
     with st.expander("🔥 Fire"):
         a_fire = st.slider("transparency", 0.0, 1.0, 0.15, key = "calfire")
         show_fire = st.toggle("Fires (2013-2023)", key = "fire", value=chatbot_toggles['fire'])
-
         show_rxburn = st.toggle("Prescribed Burns (2013-2023)", key = "rxburn", value=chatbot_toggles['rxburn'])
 
-
-        if show_fire:
-            m.add_pmtiles(url_calfire, style=fire_style, name="CALFIRE Fire Polygons (2013-2023)", opacity=a_fire, tooltip=False, fit_bounds = False)
-
-        if show_rxburn:
-            m.add_pmtiles(url_rxburn, style=rx_style, name="CAL FIRE Prescribed Burns (2013-2023)", opacity=a_fire, tooltip=False, fit_bounds = False)
-                    
-
     st.divider()
     st.markdown('<p class = "medium-font-sidebar"> Filters:</p>', help = "Apply filters to adjust what data is shown on the map.", unsafe_allow_html= True)
 
@@ -377,26 +346,10 @@ def run_sql(query,color_choice):
 
     # adding github logo 
     st.markdown(f"<div class='spacer'>{github_html}</div>", unsafe_allow_html=True)
-    
-    # st.markdown("""
-    # <p class='medium-font-sidebar'> 
-    # :left_speech_bubble: <a href='https://github.com/boettiger-lab/ca-30x30/issues' target='_blank'>Report an issue</a>
-    # </p> 
-    # """, unsafe_allow_html=True)
-
     st.markdown(":left_speech_bubble: [Get in touch or report an issue](https://github.com/boettiger-lab/ca-30x30/issues)")
 
 
-
-
-
-# Display CA 30x30 Data
-if 'out' not in locals():
-    style = get_pmtiles_style(style_options[color_choice], alpha, filter_cols, filter_vals)
-    legend, position, bg_color, fontsize = get_legend(style_options, color_choice)
-    m.add_legend(legend_dict = legend, position = position, bg_color = bg_color, fontsize = fontsize)
-    m.add_pmtiles(ca_pmtiles, style=style, name="CA", opacity=alpha, tooltip=True, fit_bounds=True)
-    
+## filter data and get map styling
 column = select_column[color_choice]
 
 select_colors = {
@@ -409,31 +362,56 @@ def run_sql(query,color_choice):
     "Access Type": access["stops"],
 }
 
-colors = (
-    ibis
-    .memtable(select_colors[color_choice], columns=[column, "color"])
-    .to_pandas()
-)
+colors = color_table(select_colors, color_choice, column)
 
 
 # get summary tables used for charts + printed table 
-# df - charts; df_tab - printed table (omits colors) 
 if 'out' not in locals():
-    df, df_tab, df_percent, df_bar_30x30 = get_summary_table(ca, column, select_colors, color_choice, filter_cols, filter_vals,colorby_vals)
+    df, df_tab, df_percent, df_bar_30x30 = get_summary_table(ca, column, select_colors, color_choice, filter_cols, filter_vals, colorby_vals)
     total_percent = (100*df_percent.percent_CA.sum()).round(2)
-
 else:
     df = get_summary_table_sql(ca, column, colors, ids)
     total_percent = (100*df.percent_CA.sum()).round(2)
 
+# build map style and legend
+if 'out' not in locals():
+    style = get_pmtiles_style(style_options[color_choice], alpha, filter_cols, filter_vals)
+
+legend,fontsize = get_legend(style_options, color_choice, df, column)
+
+# add tile/cog/pmtiles layers
+if show_richness:
+    m.add_tile_layer(url_sr, name="MOBI Species Richness", opacity=a_bio)
+if show_rsr:           
+    m.add_tile_layer(url_rsr, name="MOBI Range-Size Rarity", opacity=a_bio)
+if show_irrecoverable_carbon:
+    m.add_cog_layer(url_irr_carbon, palette="reds", name="Irrecoverable Carbon", opacity=a_climate, fit_bounds=False)
+if show_manageable_carbon:
+    m.add_cog_layer(url_man_carbon, palette="purples", name="Manageable Carbon", opacity=a_climate, fit_bounds=False)
+if show_justice40:
+    m.add_pmtiles(url_justice40, style=justice40_style, name="Justice40", opacity=a_people, tooltip=False, fit_bounds=False)
+if show_sv:
+    m.add_pmtiles(url_svi, style=svi_style, opacity=a_people, tooltip=False, fit_bounds=False)
+if show_fire:
+    m.add_pmtiles(url_calfire, style=fire_style, name="CALFIRE Fire Polygons (2013-2023)", opacity=a_fire, tooltip=False, fit_bounds=False)
+if show_rxburn:
+    m.add_pmtiles(url_rxburn, style=rx_style, name="CAL FIRE Prescribed Burns (2013-2023)", opacity=a_fire, tooltip=False, fit_bounds=False)
+
+# add main CA pmtiles layer and legend
+if 'out' not in locals():
+    m.add_pmtiles(ca_pmtiles, style=style, name="30x30 Conserved Areas (Terrestrial)",
+                  attribution="CA Nature (2024)", tooltip=True)
+    m.fit_bounds([-124.42174575, 32.53428607, -114.13077782, 42.00950367])
+    m.add_legend(title='', legend_dict=legend, fontsize=fontsize,
+                 bg_color=bg_color, position=position, shape_type=shape_type)
 
 # charts displayed based on color_by variable
 richness_chart = bar_chart(df, column, 'mean_richness', "Species Richness (2022)")
 rsr_chart = bar_chart(df, column, 'mean_rsr', "Range-Size Rarity (2022)")
 irr_carbon_chart = bar_chart(df, column, 'mean_irrecoverable_carbon', "Irrecoverable Carbon (2018)")
 man_carbon_chart = bar_chart(df, column, 'mean_manageable_carbon', "Manageable Carbon (2018)")
 fire_10_chart = bar_chart(df, column, 'mean_fire', "Fires (2013-2023)")
-rx_10_chart = bar_chart(df, column, 'mean_rxburn',"Prescribed Burns (2013-2023)")
+rx_10_chart = bar_chart(df, column, 'mean_rxburn', "Prescribed Burns (2013-2023)")
 justice40_chart = bar_chart(df, column, 'mean_disadvantaged', "Disadvantaged Communities (2021)")
 svi_chart = bar_chart(df, column, 'mean_svi', "Social Vulnerability Index (2022)")
 
@@ -446,9 +424,9 @@ def run_sql(query,color_choice):
         m.to_streamlit(height=650)
         with st.expander("🔍 View/download data"):
             if 'out' not in locals():
-                st.dataframe(df_tab, use_container_width = True)  
+                st.dataframe(df_tab, use_container_width=True)  
             else:
-                st.dataframe(out, use_container_width = True)
+                st.dataframe(out, use_container_width=True)
 
     with stats_col:
         with st.container():
@@ -462,38 +440,27 @@ def run_sql(query,color_choice):
 
             if show_richness:
                 st.altair_chart(richness_chart, use_container_width=True)
-
             if show_rsr:
                 st.altair_chart(rsr_chart, use_container_width=True)
-
             if show_irrecoverable_carbon:
                 st.altair_chart(irr_carbon_chart, use_container_width=True)
-
             if show_manageable_carbon:
                 st.altair_chart(man_carbon_chart, use_container_width=True)
-
             if show_justice40:
                 st.altair_chart(justice40_chart, use_container_width=True)
-                
             if show_sv:
                 st.altair_chart(svi_chart, use_container_width=True)
-
             if show_fire:
                 st.altair_chart(fire_10_chart, use_container_width=True)
-                
             if show_rxburn:
                 st.altair_chart(rx_10_chart, use_container_width=True)
 
 
 st.caption("***The label 'established' is inferred from the California Protected Areas Database, which may introduce artifacts. For details on our methodology, please refer to our <a href='https://github.com/boettiger-lab/ca-30x30' target='_blank'>our source code</a>.", unsafe_allow_html=True)
-
-            
-st.caption("***Under California’s 30x30 framework, only GAP codes 1 and 2 are counted toward the conservation goal.") 
+st.caption("***Under California's 30x30 framework, only GAP codes 1 and 2 are counted toward the conservation goal.") 
 
 st.divider()
 
 with open('app/footer.md', 'r') as file:
     footer = file.read()
-st.markdown(footer)
-
-
+st.markdown(footer)