Update app.py

app.py

@@ -1,96 +1,95 @@
-# Import required libraries
-import os
-import streamlit as st
-from groq import Groq
-
-# Set your API key (replace 'your_groq_api_key_here' with the actual API key)
-os.environ["GROQ_API_KEY"] = "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"
-
-# Initialize the Groq client
-client = Groq(
- api_key=os.environ.get("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)
+# 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)