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app.py

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+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
+
+st.set_page_config(page_title="Plant Growth Predictor", layout="centered")
+st.title("🌱 Plant Growth Predictor")
+
+
+@st.cache
+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(hash_funcs={Llama: lambda _: None})
+def load_mistral_model():
+    llm = Llama(
+        model_path="./models/mistral-7b-instruct-v0.1.Q5_K_S.gguf",
+        n_ctx=2048,
+        n_threads=4,
+        n_batch=512,
+        verbose=False
+    )
+    return llm
+
+llm = load_mistral_model()
+
+# Generate a description using the Mistral model
+def generate_growth_description(plant_type, 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"- Soil Type: {soil_type}\n"
+        f"- Sunlight: {sunlight_hours} hours per day\n"
+        f"- Water Frequency: {water_frequency} times per week\n"
+        f"- Fertilizer Type: {fertilizer_type}\n"
+        f"- Temperature: {temperature}°C\n"
+        f"- Humidity: {humidity}%\n"
+        f"### Response:\n"
+    )
+    output = llm(prompt, max_tokens=250, stop=["###"])
+    return output["choices"][0]["text"].strip()
+
+
+def generate_condition_image(description: str, input_image: Image.Image) -> Image.Image:
+    input_image = input_image.convert("RGB").resize((512, 512))
+    st.spinner("Generating predicted plant condition image...")
+
+st.header("Plant Info")
+plant_input_mode = st.radio("How would you like to provide plant info?", ("Type name", "Upload image"))
+plant_type = None
+uploaded_image = None
+
+if plant_input_mode == "Type name":
+    plant_type = st.selectbox("Select Plant Type", ["Basil", "Tomato", "Lettuce", "Rosemary", "Other"])
+
+elif plant_input_mode == "Upload image":
+    plant_type = st.selectbox("Select Plant Type", ["Basil", "Tomato", "Lettuce", "Rosemary", "Other"])
+    image_file = st.file_uploader("Upload an image of your plant", type=["jpg", "jpeg", "png"])
+    if image_file:
+        uploaded_image = Image.open(image_file)
+        st.image(uploaded_image, caption="Uploaded Plant Image", use_column_width=True)
+
+
+col1, col2 = st.columns(2)
+
+with col1:
+    st.header("Environmental Parameters")
+    soil_options = ["Sandy", "Clay", "Loamy", "Peaty", "Chalky", "Silty"]
+
+    soil_type = st.selectbox("Soil Type", soil_options)
+    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"]
+
+    fertilizer_type = st.selectbox("Fertilizer Type", fertilizer_options)
+    temperature = st.slider("Temperature (°C)", -10, 50, 22)
+    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")
+
+# Prediction + Description + Image Generation
+if st.button("Predict Growth Milestone and Generate Description & Image"):
+    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)
+
+            # Use uploaded image if available, else placeholder or skip image generation
+            if plant_input_mode == "Upload image" and uploaded_image:
+                manipulated_img = generate_condition_image(description, uploaded_image)
+                st.image(manipulated_img, caption="Predicted Plant Condition Image")
+            else:
+                st.info("Image prediction requires uploading a plant image.")
+
+    else:
+        st.warning("Please select or enter a plant type.")
+
+st.markdown("---")
+st.caption("Made with ❤️ by Sandwich Craftz.")

temp.py

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+import torch
+from diffusers import StableDiffusionImg2ImgPipeline
+from PIL import Image
+import requests
+from io import BytesIO
+
+# Load the pipeline
+pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
+    "runwayml/stable-diffusion-v1-5",
+    torch_dtype=torch.float16,
+).to("cuda" if torch.cuda.is_available() else "cpu")
+
+# Function to load image from local path or URL
+def load_image(path_or_url):
+    if path_or_url.startswith("http"):
+        response = requests.get(path_or_url)
+        return Image.open(BytesIO(response.content)).convert("RGB")
+    else:
+        return Image.open(path_or_url).convert("RGB")
+
+# Load original plant image
+input_image = st.file_uploader("Upload an image of your plant", type=["jpg", "jpeg", "png"])
+
+# Resize to 512x512 for best results
+input_image = input_image.resize((512, 512))
+
+# Define environmental condition prompt
+prompt = "A withered plant with brown leaves, dry soil, and signs of dehydration, after 10 days of heat and no water"
+
+# Generate the modified image
+output = pipe(prompt=prompt, image=input_image, strength=0.75, guidance_scale=7.5)
+output_image = output.images[0]
+
+# Save the result
+output_image.save("plant_future_prediction.png")