This commit is contained in:
Nicolò 2025-08-02 11:41:53 +02:00
parent 3930e3f4a2
commit 46053885ba

59
app.py
View file

@ -1,29 +1,14 @@
import os
# app.py
import streamlit as st
import pandas as pd
import joblib
from PIL import Image
import torch
from torchvision import models
from llama_cpp import Llama
from diffusers import DiffusionPipeline, StableDiffusionPipeline, DPMSolverMultistepScheduler
from image_generation import generate_image
st.set_page_config(page_title="GreenThumber", layout="centered")
st.title("🌱 GreenThumber")
@st.cache_resource
def load_imagenet_labels():
import urllib
url = "https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt"
response = urllib.request.urlopen(url)
labels = [line.strip() for line in response.read().decode("utf-8").split("\n")]
return labels
labels = load_imagenet_labels()
# Load Mistral LLM via llama-cpp-python with custom hash to avoid Streamlit caching issues
@st.cache_resource(hash_funcs={Llama: lambda _: None})
def load_mistral_model():
llm = Llama(
@ -39,13 +24,14 @@ llm = load_mistral_model()
# Generate a description using the Mistral model
def generate_growth_description(plant_type, soil_type, sunlight_hours, water_frequency,
def generate_growth_description(plant_type, plant_age, soil_type, sunlight_hours, water_frequency,
fertilizer_type, temperature, humidity, days, additional_info):
prompt = (
f" Instruction:\n"
f"You are a botanical expert. Describe how a {plant_type} plant will likely look in {days} days "
f"based on these conditions:\n"
f"Important additional conditions: {additional_info}\n"
f"Important environment conditions: {additional_info}\n"
f"- Plant age: {plant_age}\n"
f"- Soil Type: {soil_type}\n"
f"- Sunlight: {sunlight_hours} hours per day\n"
f"- Water Frequency: {water_frequency} times per week\n"
@ -66,10 +52,11 @@ uploaded_image = None
if plant_input_mode == "Type name":
plant_type = st.selectbox("Select Plant Type", ["Basil", "Tomato", "Lettuce"])
plant_age = st.number_input("Enter Plant Age (in days)", min_value=1, max_value=365, value=25)
elif plant_input_mode == "Upload image":
plant_type = st.selectbox("Select Plant Type", ["Basil", "Tomato", "Lettuce"])
plant_age = st.number_input("Enter Plant Age (in days)", min_value=1, max_value=365, value=30)
plant_age = st.number_input("Enter Plant Age (in days)", min_value=1, max_value=365, value=25)
image_file = st.file_uploader("Upload an image of your plant", type=["jpg", "jpeg", "png"])
if image_file:
uploaded_image = Image.open(image_file)
@ -86,7 +73,6 @@ with col1:
sunlight_hours = st.slider("Sunlight Hours per day", 0, 24, 6)
water_frequency = st.slider("Water Frequency (times per week)", 0, 14, 3)
# --- Column 2: Environmental Parameters
with col2:
fertilizer_options = ["Organic", "Chemical", "None"]
@ -95,27 +81,24 @@ with col2:
humidity = st.slider("Humidity (%)", 0, 100, 60)
days = st.slider("Prediction Interval (in days)", min_value=1, max_value=30, value=7)
additional_info = st.text_area("Feel free to include any additional detail")
additional_info = st.text_area("Feel free to include any additional detail", "- e.g. 'I'm adding compost to the soil.'", height=100)
# Prediction + Description + Image Generation
if st.button("Start Prediction"):
if plant_type and plant_type.strip() != "":
if plant_input_mode == "Upload image" and uploaded_image is None:
st.warning("Please upload a plant image to proceed.")
else:
with st.spinner("Analyzing data and generating description..."):
description = generate_growth_description(
plant_type, soil_type, sunlight_hours, water_frequency,
fertilizer_type, temperature, humidity, days, additional_info
)
st.subheader(f"📝 Predicted Plant Condition in {days} Days:")
st.write(description)
manipulated_img = generate_image(plant_type, description, plant_age)
st.image(manipulated_img, caption="Predicted Plant Condition Image")
#if st.button("Start Prediction"):
if plant_type and plant_type.strip() != "":
with st.spinner("Analyzing data and generating description..."):
description = generate_growth_description(
plant_type, plant_age, soil_type, sunlight_hours, water_frequency,
fertilizer_type, temperature, humidity, days, additional_info
)
st.subheader(f"📝 Predicted Plant Condition in {days} Days:")
st.write(description)
else:
st.warning("Please select or enter a plant type.")
manipulated_img = generate_image(plant_type, description, plant_age)
st.image(manipulated_img, caption="Predicted Plant Condition Image")
# else:
# st.warning("Please select or enter a plant type.")