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import gradio as gr
from folium import Map
import numpy as np
from ast import literal_eval
import pandas as pd
from gradio_folium import Folium
import folium
from huggingface_hub import InferenceClient
from geopy.geocoders import Nominatim
from examples import (
description_sf,
output_example_sf,
description_loire,
output_example_loire,
df_examples
)
geolocator = Nominatim(user_agent="HF-trip-planner")
def get_coordinates(address):
location = geolocator.geocode(address)
if location:
return (location.latitude, location.longitude)
else:
return None
repo_id = "mistralai/Mixtral-8x7B-Instruct-v0.1"
llm_client = InferenceClient(model=repo_id, timeout=180)
def generate_key_points(text):
prompt = f"""
Please generate a set of key geographical points for the following description: {text}, as a json list of less than 10 dictionnaries with the following keys: 'name', 'description'. Precise the full location in the 'name' if there is a possible ambiguity.
Generally try to minimze the distance between locations. Always think of the transportation means that you want to use, and the timing: morning, afternoon, where to sleep.
Only generate a 'Thought:' and a 'Key points:' sections, nothing else.
For instance:
Description: {description_sf}
Thought: {output_example_sf}
Description: {description_loire}
Thought: {output_example_loire}
Now begin. You can make the descriptions a bit more verbose than in the examples.
Description: {text}
Thought:
"""
return llm_client.text_generation(prompt, max_new_tokens=2000)
def parse_llm_output(output):
rationale = "Thought: " + output.split("Key points:")[0]
key_points = output.split("Key points:")[1]
output = key_points.replace(" ", "")
parsed_output = literal_eval(output)
dataframe = pd.DataFrame.from_dict(parsed_output)
return dataframe, rationale
def get_coordinates_row(row):
coords = get_coordinates(row["name"])
if coords is not None:
row["lat"], row["lon"] = coords
return row
def create_map_from_markers(dataframe):
dataframe = dataframe.apply(get_coordinates_row, axis=1)
f_map = Map(
location=[dataframe["lat"].mean(), dataframe["lon"].mean()],
zoom_start=5,
tiles="CartoDB Voyager",
)
for _, row in dataframe.iterrows():
if np.isnan(row["lat"]) or np.isnan(row["lon"]):
continue
marker = folium.CircleMarker(
location=[row["lat"], row["lon"]],
radius=10,
popup=folium.Popup(
f"<h4>{row['name']}</h4><p>{row['description']}</p>", max_width=450
),
fill=True,
fill_color="blue",
fill_opacity=0.6,
color="blue",
weight=1,
)
marker.add_to(f_map),
bounds = [[row["lat"], row["lon"]] for _, row in dataframe.iterrows()]
f_map.fit_bounds(bounds, padding=(100, 100))
return f_map
def run_display(text):
output = generate_key_points(text)
dataframe, rationale = parse_llm_output(output)
map = create_map_from_markers(dataframe)
return map, rationale
df_examples = pd.DataFrame.from_dict(
[
{"description": description_loire, "output": output_example_loire},
{"description": description_aligned, "output": output_example_aligned},
{"description": description_chinatown, "output": output_example_chinatown},
{"description": description_taiwan, "output": output_example_taiwan},
]
)
def select_example(df, data: gr.SelectData):
row = df.iloc[data.index[0], :]
dataframe, rationale = parse_llm_output(row["output"])
return row["description"], create_map_from_markers(dataframe), rationale
with gr.Blocks(
theme=gr.themes.Soft(
primary_hue=gr.themes.colors.yellow,
secondary_hue=gr.themes.colors.blue,
)
) as demo:
gr.Markdown("# 🗺️ LLM trip planner (based on Mixtral)")
text = gr.Textbox(
label="Describe your trip here:",
value=description_sf,
)
button = gr.Button()
gr.Markdown("### LLM Output 👇\n_Click the map to see information about the places._")
# Get initial map and rationale
example_dataframe, example_rationale = parse_llm_output(output_example_sf)
display_rationale = gr.Markdown(example_rationale)
starting_map = create_map_from_markers(example_dataframe)
map = Folium(value=starting_map, height=700, label="Chosen locations")
button.click(run_display, inputs=[text], outputs=[map, display_rationale])
gr.Markdown("### Other examples")
clickable_examples = gr.DataFrame(value=df_examples, height=200)
clickable_examples.select(
select_example, clickable_examples, outputs=[text, map, display_rationale]
)
if __name__ == "__main__":
demo.launch() |