Insect_HFspace_Streamlit_App.py

# -*- coding: utf-8 -*-
"""
Created on Tue Nov 18 09:07:10 2025

@author: THYAGHARAJAN
"""

import streamlit as st
import tensorflow as tf
import numpy as np
from PIL import Image
from huggingface_hub import hf_hub_download, list_repo_files
import os
os.environ["STREAMLIT_SERVER_ENABLE_CORS"] = "false"
os.environ["STREAMLIT_SERVER_ENABLE_XSRF_PROTECTION"] = "false"
# ------------------------------
# CONFIGURATION
# ------------------------------
REPO_ID = "kkthyagharajan/KKT-HF-TransferLearning-Models"     # <<< CHANGE THIS
IMG_SIZE = (300, 300)

st.set_page_config(page_title="Insect Classifier", layout="wide")

# Cache dictionaries
@st.cache_resource
def load_tf_model(model_path):
    return tf.keras.models.load_model(model_path, compile=False)

@st.cache_resource
def load_class_names(model_dir):
    class_file = hf_hub_download(repo_id=REPO_ID, filename=f"{model_dir}/class_names.txt")
    with open(class_file, "r") as f:
        return [x.strip() for x in f.read().split(",")]

# ----------------------------------
# Helper Functions
# ----------------------------------
def get_available_models():
    """Return mapping: model_dir → model file (.h5 preferred over .keras)."""
    files = list_repo_files(REPO_ID)
    models = {}

    # Prefer .h5
    for file in files:
        if file.endswith(".h5"):
            dir = file.split("/")[0]
            models[dir] = file

    # Use .keras only if .h5 missing
    for file in files:
        if file.endswith(".keras"):
            dir = file.split("/")[0]
            if dir not in models:
                models[dir] = file

    return models

def get_sample_images(model_dir):
    """List sample images inside model_dir/sample_images/"""
    files = list_repo_files(REPO_ID)
    sample_imgs = []
    prefix = f"{model_dir}/sample_images/"

    for f in files:
        if f.startswith(prefix) and f.lower().endswith((".jpg", ".jpeg", ".png")):
            sample_imgs.append(f.replace(prefix, ""))  

    return sample_imgs

def load_sample_image(model_dir, image_name):
    """Download sample image."""
    path = hf_hub_download(repo_id=REPO_ID, filename=f"{model_dir}/sample_images/{image_name}")
    return Image.open(path)

def preprocess(img):
    img = img.resize(IMG_SIZE)
    arr = np.array(img) / 255.0
    arr = arr.reshape(1, IMG_SIZE[0], IMG_SIZE[1], 3)
    return arr

# ----------------------------------
# UI Layout
# ----------------------------------
st.title("🦋 Insect Classification System")
st.markdown("""
### A Multi-Model Deep Learning Web App  
Developed by **Dr. Thyagharajan K K, Professor & Dean (Research)**  
RMD Engineering College  
""")

col1, col2 = st.columns([1, 1])

# ----------------------------------
# LEFT PANEL
# ----------------------------------
with col1:
    st.subheader("1️⃣ Select Model")
    models = get_available_models()

    if not models:
        st.error("No models found in HuggingFace repo.")
        st.stop()

    model_choice = st.selectbox("Choose a model", list(models.keys()))

    st.subheader("2️⃣ Choose Image Source")
    input_mode = st.radio("Select input method:", ["Upload Image", "Use Sample Image"])

    input_image = None

    # Upload
    if input_mode == "Upload Image":
        uploaded = st.file_uploader("Upload image", type=["jpg", "jpeg", "png"])
        if uploaded:
            input_image = Image.open(uploaded)

    # Sample Images
    else:
        sample_images = get_sample_images(model_choice)
        if sample_images:
            selected_sample = st.selectbox("Choose sample image", sample_images)
            if selected_sample:
                input_image = load_sample_image(model_choice, selected_sample)
                st.image(input_image, caption="Sample Image", width=250)
        else:
            st.warning("No sample images found for this model.")

    st.markdown("---")
    predict_btn = st.button("🔍 Predict", use_container_width=True)

# ----------------------------------
# RIGHT PANEL
# ----------------------------------
with col2:
    st.subheader("📊 Prediction Results")

    if predict_btn:
        if input_image is None:
            st.error("Please upload or select an image.")
        else:
            # Show image
            st.image(input_image, caption="Input Image", width=300)

            # Load model
            model_path = hf_hub_download(repo_id=REPO_ID, filename=models[model_choice])
            model = load_tf_model(model_path)
            class_names = load_class_names(model_choice)

            # Predict
            arr = preprocess(input_image)
            preds = model.predict(arr, verbose=0)[0]

            idx = np.argmax(preds)
            predicted = class_names[idx]

            st.success(f"### 🟩 Predicted: **{predicted}** ({preds[idx]*100:.2f}%)")

            # Top-3 Predictions
            st.subheader("Top 3 Predictions")
            top3 = preds.argsort()[-3:][::-1]
            for i in top3:
                st.write(f"**{class_names[i]}** — {preds[i]*100:.2f}%")

# Footer
st.markdown("---")
st.markdown("""
**Developed by:** Dr. Thyagharajan K K  
**Professor & Dean (Research)**  
RMD Engineering College  
📧 **kkthyagharajan@yahoo.com**
""")