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import os
import pandas as pd
from datetime import datetime
from dotenv import load_dotenv
from langchain_core.output_parsers import StrOutputParser
from langchain.prompts import ChatPromptTemplate
from langchain.chat_models import ChatOpenAI
from prompts.summary_prompt import (
meterological_data_summary_prompt,
agricultural_yield_comparison_prompt
)
load_dotenv()
def get_meterological_summary(scenario: str, temperature_df: pd.DataFrame, rain_df: pd.DataFrame, irradiance_df: pd.DataFrame) -> str:
today = datetime.today().strftime("%Y/%m/%d")
temp_data = temperature_df.head(len(temperature_df)).to_string(index=False)
rain_data = rain_df.head(len(rain_df)).to_string(index=False)
irradiance_data = irradiance_df.head(len(irradiance_df)).to_string(index=False)
llm = ChatOpenAI(
model="gpt-4o",
temperature=0,
max_tokens=None,
timeout=None,
max_retries=2,
api_key=os.environ.get("OPENAI_API_KEY")
)
output_parser = StrOutputParser()
prompt = ChatPromptTemplate.from_messages(
[
("system", meterological_data_summary_prompt),
("human", "Je veux un résumé de ces prévisions métérologique: les données de temperature {temp_data}, les données de précipitation {rain_data}, les données de radiance solaire {irradiance_data}")
]
)
chain = prompt | llm | output_parser
response = chain.invoke({
"scenario": scenario,
"today": today,
"temp_data": temp_data,
"rain_data": rain_data,
"irradiance_data": irradiance_data
})
return output_parser.parse(response)
def get_agricultural_yield_comparison(culture: str,
region:str,
historical_yield_df: pd.DataFrame,
forecast_yield_df: pd.DataFrame,
soil_df: pd.DataFrame,
climate_df: pd.DataFrame,
water_df: pd.DataFrame,
water_df_pv: pd.DataFrame):
historical_yield = historical_yield_df.head(len(historical_yield_df)).to_string(index=False)
agricultural_yield = forecast_yield_df.head(len(forecast_yield_df)).to_string(index=False)
soil_data = soil_df.head(len(soil_df)).to_string(index=False)
water_data = water_df.head(len(water_df)).to_string(index=False)
water_data_pv = water_df_pv.head(len(water_df_pv)).to_string(index=False)
climate_data = climate_df.head(len(climate_df)).to_string(index=False)
llm = ChatOpenAI(
model="gpt-4o",
temperature=0,
max_tokens=None,
timeout=None,
max_retries=2,
api_key=os.environ.get("OPENAI_API_KEY")
)
output_parser = StrOutputParser()
prompt = ChatPromptTemplate.from_messages(
[
("system", agricultural_yield_comparison_prompt),
("human", "Je suis agriculteur et je cultive de la {culture} à {region}. Voilà les caractéristiques du sol dans ma région {soil_data} et voilà l'historique de mon rendement {historical_yield} et projections du rendement ma culture avec et sans ombrage {agricultural_yield}. J'ai aussi les prévisions du stress hydrique sans ombrage {water_data} et avec ombrage {water_data_pv} et des données climatiques {climate_data}. " )
]
)
chain = prompt | llm | output_parser
response = chain.invoke({
"culture": culture,
"region": region,
"soil_data": soil_data,
"water_data": water_data,
"water_data_pv": water_data_pv,
"climate_data": climate_data,
"agricultural_yield": agricultural_yield,
"historical_yield": historical_yield
})
return output_parser.parse(response) |