From 46053885ba36512a0b80bd8bbee9874f7ce4b8fb Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Nicol=C3=B2?= <nicolotonello@gmail.com>
Date: Sat, 2 Aug 2025 11:41:53 +0200
Subject: [PATCH] init

---
 app.py | 59 +++++++++++++++++++++-------------------------------------
 1 file changed, 21 insertions(+), 38 deletions(-)

diff --git a/app.py b/app.py
index fd133803d..cc1f393cd 100644
--- a/app.py
+++ b/app.py
@@ -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.")