app.py

import gradio as gr
from google import generativeai
import random


API_KEYS = [
    "AIzaSyBvtwP2ulNHPQexfPhhR13U30pvF2OswrU",
    "AIzaSyD0dLXPPrZmLbnHOj3f9twHmT_PZc15wMo",

]


api_key = random.choice(API_KEYS)
generativeai.configure(api_key=api_key)

# Initialize Gemini client
 # Replace with your key
model = generativeai.GenerativeModel("gemini-2.0-flash")

def check_chicken_suitability(location):
    prompt = f"Give me the average prdeicted temperature in Celsius for the next 7 days in {location}.Just make a prediction. Respond with only 7 comma-separated numbers."
    
    try:
        response = model.generate_content(prompt)
        raw = response.text.strip()

        # Extract temperatures
        temps = [float(t.strip()) for t in raw.split(",") if t.strip().replace(".", "", 1).isdigit()]
        
        if len(temps) != 7:
            return f"❌ Couldn't get 7 temperatures. Gemini returned: {raw}"
        
        avg_temp = sum(temps) / 7
        result = f"📍 Location: {location}\n📊 Average Temperature of the next seven days: {avg_temp:.2f}°C\n"

        if avg_temp > 30:
            result += "\n⚠️ The average temperature expected to exeed 35 C over the next 7 days. It is NOT suitable to put young poultry chicks."
        else:
            result += "\n✅ The average temperature expected to NOT exeed 35 C over the next 7 days. It is suitable to put young chickens."

        return result

    except Exception as e:
        return f"❌ Error occurred: {str(e)}"

# Gradio Interface
iface = gr.Interface(
    fn=check_chicken_suitability,
    inputs=gr.Textbox(label="Enter Location"),
    outputs=gr.Textbox(label="Result"),
    title="Chicken Suitability Checker 🌡️🐥",
    description="Enter your location to check if the average temperature over the next 7 days is suitable for placing young chickens."
)

if __name__ == "__main__":
    iface.launch()