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0a7d404 a5457fb 0d8c0ab d3b1885 2a138be d3b1885 9625337 d3b1885 9625337 d3b1885 2a138be d3b1885 2a138be d3b1885 2a138be d3b1885 2a138be d3b1885 2a138be d3b1885 2a138be d3b1885 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 | import streamlit as st
DEBUG_MINIMAL = False # Set to False to run the full app
if DEBUG_MINIMAL:
st.write("Streamlit app file loaded")
st.title("Plant Disease Classifier")
st.write("Minimal debug mode: Streamlit is working!")
else:
from PIL import Image
import torch
import io
import os
import json
from transformers import AutoImageProcessor, AutoModelForImageClassification
from model_paths import resolve_model_dir
# Model directory
MODEL_DIR = resolve_model_dir()
st.title("Plant Disease Classifier")
st.write("App is starting...")
st.write("b4 uploaded_file")
uploaded_file = st.file_uploader(
"Upload a plant image", type=["jpg", "jpeg", "png"])
st.write("after uploaded_file")
if uploaded_file is not None:
# Load model and processor only when needed
@st.cache_resource
def load_model_and_processor():
model = AutoModelForImageClassification.from_pretrained(MODEL_DIR)
processor = AutoImageProcessor.from_pretrained(MODEL_DIR)
model.eval()
device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
return model, processor, device
st.write("Loading model and processor...")
model, processor, device = load_model_and_processor()
# Load class mapping
with open(os.path.join(MODEL_DIR, "class_mapping.json")) as f:
class_mapping = json.load(f)
idx_to_class = {v: k for k, v in class_mapping.items()}
st.write("Model and processor loaded.")
image = Image.open(uploaded_file).convert("RGB")
st.image(image, caption="Uploaded Image", use_column_width=True)
st.write("Image uploaded and displayed.")
inputs = processor(images=image, return_tensors="pt")
inputs = {k: v.to(device) for k, v in inputs.items()}
st.write("Inputs processed, running inference...")
with torch.no_grad():
outputs = model(**inputs)
pred_idx = torch.argmax(outputs.logits, dim=1).item()
pred_class = idx_to_class[pred_idx]
confidence = torch.softmax(outputs.logits, dim=1)[
0, pred_idx].item()
st.markdown(f"**Prediction:** {pred_class}")
st.markdown(f"**Confidence:** {confidence:.2f}")
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