app.py

import streamlit as st
import requests
from transformers import pipeline

# Cache the AI models to avoid reloading them on every interaction
@st.cache_resource
def load_qa_model():
    try:
        return pipeline("question-answering", model="distilbert-base-cased-distilled-squad", framework="pt")
    except Exception as e:
        st.error(f"Error loading QA model: {e}")
        return None

@st.cache_resource
def load_text_generation_model():
    try:
        return pipeline("text-generation", model="gpt2", framework="pt")
    except Exception as e:
        st.error(f"Error loading text generation model: {e}")
        return None

# Initialize the AI models
qa_pipeline = load_qa_model()
text_generation_pipeline = load_text_generation_model()

# Cache the plant data fetching function to avoid redundant API calls
@st.cache_data(ttl=3600)  # Cache for 1 hour
def fetch_plant_data(plant_name):
    url = f"https://openfarm.cc/api/v1/crops?filter={plant_name}"
    try:
        response = requests.get(url)
        response.raise_for_status()  # Raise an error for bad status codes
        data = response.json()
        if data.get("data"):
            # Get the first result (most likely match)
            plant_info = data["data"][0]["attributes"]
            return {
                "name": plant_info.get("name", plant_name),
                "description": plant_info.get("description", "No description available."),
                "image_url": plant_info.get("main_image_path", None),
                "sun_requirements": plant_info.get("sun_requirements", "No information available."),
                "watering": plant_info.get("watering", "No specific instructions available."),
                "growth_rate": plant_info.get("growth_rate", "No information available."),
                "spacing": plant_info.get("spacing", "No information available."),
                "sowing_method": plant_info.get("sowing_method", "No information available."),
            }
        else:
            st.warning(f"No data found for the plant: {plant_name}")
            return None
    except requests.exceptions.RequestException as e:
        st.error(f"Error fetching plant data: {e}")
        return None

# Function to generate watering instructions using AI
def refine_watering_instructions(plant_name, basic_instructions=None):
    prompt = (
        f"Provide detailed watering instructions for the plant '{plant_name}'. "
        f"Base your response on the following basic instructions: '{basic_instructions}'. "
        "Include information on how often to water, the amount of water needed, and any seasonal variations."
    )
    try:
        result = text_generation_pipeline(prompt, max_length=200, num_return_sequences=1)
        return result[0]["generated_text"]
    except Exception as e:
        st.error(f"Error generating watering instructions: {e}")
        return "Unable to generate watering instructions at this time."

# Function to check if the image URL is valid
def is_valid_image_url(url):
    if url is None:
        return False
    try:
        response = requests.head(url)
        return response.status_code == 200
    except requests.exceptions.RequestException:
        return False

# Streamlit app
def main():
    st.title("🌱 AI-Powered Plant Care Guide")

    # Section 1: Search for plant care information
    st.header("Search for Plant Care Information")
    user_input = st.text_input("Enter the name of a plant", "")

    if st.button("Search"):
        if user_input.strip():
            with st.spinner("Fetching plant data..."):
                plant_info = fetch_plant_data(user_input)

            if plant_info:
                st.success(f"Found information for {plant_info['name']}!")
                
                # Refine or generate AI-based watering instructions
                with st.spinner("Generating detailed watering instructions..."):
                    plant_info["watering"] = refine_watering_instructions(user_input, basic_instructions=plant_info["watering"])

                st.subheader(f"Care Instructions for {plant_info['name']}")
                st.write(f"**Description:** {plant_info['description']}")
                st.write(f"**Sun Requirements:** {plant_info['sun_requirements']}")
                st.write(f"**Growth Rate:** {plant_info['growth_rate']}")
                st.write(f"**Spacing:** {plant_info['spacing']}")
                st.write(f"**Sowing Method:** {plant_info['sowing_method']}")
                st.write(f"**Watering Instructions:** {plant_info['watering']}")
                
                # Display the image if the URL is valid
                if plant_info["image_url"] and is_valid_image_url(plant_info["image_url"]):
                    st.image(plant_info["image_url"], caption=plant_info["name"], width=300)
                else:
                    st.warning("No valid image available for this plant.")
            else:
                st.warning("No information found for the specified plant. Please try another name.")
        else:
            st.warning("Please enter a plant name to search.")

if __name__ == "__main__":
    main()