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import pandas as pd
import folium
from folium import IFrame
import plotly.graph_objects as go
from APIs.geolocation import get_geolocation, get_risques
from APIs.meteo import get_info_meteo
import os
from dotenv import load_dotenv
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.llms.mistralai import MistralAI
from llama_index.embeddings.mistralai import MistralAIEmbedding
from llama_index.core import Settings
from llama_index.core.query_engine import RetrieverQueryEngine
load_dotenv()
Settings.llm = MistralAI(
max_tokens=1024,
api_key=os.environ.get('MISTRAL_API_KEY')
)
Settings.embed_model = MistralAIEmbedding(
model_name="mistral-embed",
api_key=os.environ.get('MISTRAL_API_KEY')
)
documents = SimpleDirectoryReader("documents").load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine(similarity_top_k=15)
def on_init(lat, lon, address):
print('titi')
map_html, fig = show_map(lat, lon, address)
df = get_info_meteo(lat, lon)
avg_temp = df.loc[:, 'avg_hourly_soil_temperature_0cm_C'].mean()
avg_humidity = df.loc[:, 'avg_hourly_soil_moisture_0_to_1cm_m³'].mean()
print('toto')
return map_html, fig, avg_temp, avg_humidity
def print_conversation(chat_history):
for question, response in chat_history:
print('Question: ', end='\n')
print(question, end='\n')
print('Response: ', end='\n')
print(response, end='\n')
def respond(elevage, temperature, humidite, meteo, message, chat_history):
PROMPT = f"""
Tu es un chatbot qui répond en français, qui doit aider à déterminer les risques parasitaires d'élevage en fonction des conditions météorologiques.
Voici les conditions:
- élevage: {elevage}
- température moyenne : {temperature}
- humidité moyenne : {humidite}
- météo : {meteo}
{message}
Donne-moi ensuite les solutions de prévention et de traitement pour chacun d'eux indépendamment du tableau.
Tu dois impérativement répondre en français"""
reponse = query_engine.query(PROMPT)
chat_history.append((message, str(reponse)))
print_conversation(chat_history)
return '', chat_history
def get_and_plot_meteo(lat, lon):
df = get_info_meteo(lat, lon)
fig = go.Figure()
fig.add_trace(go.Scatter(x=df['date'], y=df['daily_temperature_2m_max_C'],
name="Température °C", mode="lines+markers"))
fig.add_trace(go.Scatter(x=df['date'], y=df['avg_hourly_relative_humidity_2m_%'],
name="Humidité moyenne en %", mode="lines+markers", yaxis="y2"))
fig.update_layout(
title="Température maximale et humidité moyenne journalière",
xaxis_title="Date",
yaxis_title="Température (°C)",
yaxis2=dict(
title="Humidity (%)",
overlaying="y",
side="right"
)
)
return fig
def show_map(lat, lon, address):
if address:
lat_tmp, lon_tmp, code_insee = get_geolocation(address, None, None)
risques = get_risques(code_insee=code_insee)
if lat_tmp or lon_tmp:
lat, lon = lat_tmp, lon_tmp
else:
return "Adress not found. Please enter a valid address", ""
if lat and lon:
lat_tmp, lon_tmp, code_insee = get_geolocation(None, lat, lon)
risques = get_risques(code_insee=code_insee)
# Create a map centered at the input coordinates
location_map = folium.Map(location=[lat, lon], zoom_start=14)
folium.Marker([lat, lon], popup=risques).add_to(location_map)
map_html = location_map._repr_html_()
fig = get_and_plot_meteo(lat, lon)
return map_html, fig
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