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# Import required libraries
import streamlit as st
from groq import Groq
# Set your API key (replace 'your_groq_api_key_here' with the actual API key)
GROQ_API_KEY = "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"
# Initialize the Groq client
client = Groq(api_key=GROQ_API_KEY)
# Function to handle predictions
def predict_yield(climate_zone=None, region=None, yield_units=None, farm_size=None, fertilizer_rate=None,
fertilizer_type=None, historical_weather=None, temperature=None, soil_moisture=None,
soil_type=None, weather_condition=None, crop_type=None, irrigation_method=None,
prediction_period=None, custom_prompt=None):
try:
if custom_prompt:
prompt = custom_prompt
else:
# Construct the prompt for the model using individual inputs
prompt = (
f"Predict the agricultural yield for a farm in the {climate_zone} climate zone, "
f"located in the {region} region. The farm size is {farm_size} acres, and the desired yield units are {yield_units}. "
f"The fertilizer application rate is {fertilizer_rate} using {fertilizer_type}. Historical weather data indicates {historical_weather}. "
f"The average temperature is {temperature} degrees, soil moisture levels are {soil_moisture}, and the soil type is {soil_type}. "
f"The current weather condition is {weather_condition}. The crop type is {crop_type}, and the irrigation method used is {irrigation_method}. "
f"The yield prediction period is {prediction_period}."
)
# Call the Groq API
chat_completion = client.chat.completions.create(
messages=[
{
"role": "user",
"content": prompt,
}
],
model="llama3-8b-8192",
)
# Return the response
return chat_completion.choices[0].message.content
except Exception as e:
return f"An error occurred during prediction: {e}"
# Streamlit Interface
st.title("Agricultural Yield Prediction App")
st.write("Predict agricultural yield based on various factors.")
# Sidebar for input method selection
st.sidebar.title("Input Method")
input_method = st.sidebar.radio("Choose input method:", ("Use Custom Prompt", "Use Parameters"))
if input_method == "Use Parameters":
# Sidebar inputs for parameters
st.sidebar.title("Input Parameters")
climate_zone = st.sidebar.text_input("Climate Zone")
region = st.sidebar.text_input("Region")
yield_units = st.sidebar.text_input("Desired Yield Units (e.g., tons per acre, bushels per acre)")
farm_size = st.sidebar.text_input("Farm Size (acres or hectares)")
fertilizer_rate = st.sidebar.text_input("Fertilizer Application Rate")
fertilizer_type = st.sidebar.text_input("Fertilizer Type")
historical_weather = st.sidebar.text_input("Historical Weather Data")
temperature = st.sidebar.text_input("Temperature (degrees)")
soil_moisture = st.sidebar.text_input("Soil Moisture Levels")
soil_type = st.sidebar.text_input("Soil Type")
weather_condition = st.sidebar.text_input("Weather Condition")
crop_type = st.sidebar.text_input("Crop Type")
irrigation_method = st.sidebar.text_input("Irrigation Method")
prediction_period = st.sidebar.text_input("Yield Prediction Period (e.g., weekly, monthly, seasonal)")
custom_prompt = None
else:
# Sidebar input for custom prompt
st.sidebar.title("Custom Prompt")
custom_prompt = st.sidebar.text_area("Enter your custom prompt here", value="Enter your prompt...")
climate_zone = region = yield_units = farm_size = fertilizer_rate = fertilizer_type = historical_weather = None
temperature = soil_moisture = soil_type = weather_condition = crop_type = irrigation_method = prediction_period = None
# Main page layout for buttons and output
col1, col2 = st.columns([1, 2])
# Clear button functionality
if col2.button("Clear"):
st.rerun()
# Predict button and display result
if col1.button("Predict Yield"):
prediction = predict_yield(climate_zone, region, yield_units, farm_size, fertilizer_rate, fertilizer_type,
historical_weather, temperature, soil_moisture, soil_type, weather_condition,
crop_type, irrigation_method, prediction_period, custom_prompt)
st.write("Predicted Yield:", prediction)
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