124 lines
5 KiB
Python
124 lines
5 KiB
Python
import streamlit as st
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import pandas as pd
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import joblib
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from PIL import Image
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import torch
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from torchvision import models
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from llama_cpp import Llama
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from diffusers import DiffusionPipeline
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st.set_page_config(page_title="Plant Growth Predictor", layout="centered")
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st.title("🌱 Plant Growth Predictor")
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@st.cache
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def load_imagenet_labels():
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import urllib
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url = "https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt"
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response = urllib.request.urlopen(url)
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labels = [line.strip() for line in response.read().decode("utf-8").split("\n")]
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return labels
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labels = load_imagenet_labels()
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# Load Mistral LLM via llama-cpp-python with custom hash to avoid Streamlit caching issues
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@st.cache(hash_funcs={Llama: lambda _: None})
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def load_mistral_model():
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llm = Llama(
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model_path="./models/mistral-7b-instruct-v0.1.Q5_K_S.gguf",
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n_ctx=2048,
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n_threads=4,
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n_batch=512,
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verbose=False
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)
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return llm
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llm = load_mistral_model()
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# Generate a description using the Mistral model
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def generate_growth_description(plant_type, soil_type, sunlight_hours, water_frequency,
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fertilizer_type, temperature, humidity, days, additional_info):
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prompt = (
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f" Instruction:\n"
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f"You are a botanical expert. Describe how a {plant_type} plant will likely look in {days} days "
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f"based on these conditions:\n"
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f"Important additional conditions: {additional_info}\n"
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f"- Soil Type: {soil_type}\n"
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f"- Sunlight: {sunlight_hours} hours per day\n"
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f"- Water Frequency: {water_frequency} times per week\n"
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f"- Fertilizer Type: {fertilizer_type}\n"
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f"- Temperature: {temperature}°C\n"
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f"- Humidity: {humidity}%\n"
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f"### Response:\n"
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)
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output = llm(prompt, max_tokens=250, stop=["###"])
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return output["choices"][0]["text"].strip()
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def generate_condition_image(description: str, input_image: Image.Image) -> Image.Image:
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input_image = input_image.convert("RGB").resize((512, 512))
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st.spinner("Generating predicted plant condition image...")
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st.header("Plant Info")
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plant_input_mode = st.radio("How would you like to provide plant info?", ("Type name", "Upload image"))
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plant_type = None
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uploaded_image = None
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if plant_input_mode == "Type name":
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plant_type = st.selectbox("Select Plant Type", ["Basil", "Tomato", "Lettuce", "Rosemary", "Other"])
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elif plant_input_mode == "Upload image":
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plant_type = st.selectbox("Select Plant Type", ["Basil", "Tomato", "Lettuce", "Rosemary", "Other"])
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image_file = st.file_uploader("Upload an image of your plant", type=["jpg", "jpeg", "png"])
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if image_file:
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uploaded_image = Image.open(image_file)
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st.image(uploaded_image, caption="Uploaded Plant Image", use_column_width=True)
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col1, col2 = st.columns(2)
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with col1:
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st.header("Environmental Parameters")
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soil_options = ["Sandy", "Clay", "Loamy", "Peaty", "Chalky", "Silty"]
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soil_type = st.selectbox("Soil Type", soil_options)
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sunlight_hours = st.slider("Sunlight Hours per day", 0, 24, 6)
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water_frequency = st.slider("Water Frequency (times per week)", 0, 14, 3)
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# --- Column 2: Environmental Parameters
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with col2:
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fertilizer_options = ["Organic", "Chemical", "None"]
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fertilizer_type = st.selectbox("Fertilizer Type", fertilizer_options)
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temperature = st.slider("Temperature (°C)", -10, 50, 22)
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humidity = st.slider("Humidity (%)", 0, 100, 60)
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days = st.slider("Prediction Interval (in days)", min_value=1, max_value=30, value=7)
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additional_info = st.text_area("Feel free to include any additional detail")
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# Prediction + Description + Image Generation
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if st.button("Predict Growth Milestone and Generate Description & Image"):
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if plant_type and plant_type.strip() != "":
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if plant_input_mode == "Upload image" and uploaded_image is None:
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st.warning("Please upload a plant image to proceed.")
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else:
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with st.spinner("Analyzing data and generating description..."):
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description = generate_growth_description(
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plant_type, soil_type, sunlight_hours, water_frequency,
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fertilizer_type, temperature, humidity, days, additional_info
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)
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st.subheader(f"📝 Predicted Plant Condition in {days} Days:")
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st.write(description)
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# Use uploaded image if available, else placeholder or skip image generation
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if plant_input_mode == "Upload image" and uploaded_image:
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manipulated_img = generate_condition_image(description, uploaded_image)
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st.image(manipulated_img, caption="Predicted Plant Condition Image")
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else:
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st.info("Image prediction requires uploading a plant image.")
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else:
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st.warning("Please select or enter a plant type.")
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st.markdown("---")
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st.caption("Made with ❤️ by Sandwich Craftz.")
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