Delete src

src/image_classifier_app.py

@@ -1,93 +0,0 @@
-import streamlit as st
-import torch
-import torch.nn as nn
-import torchvision.transforms as transforms
-from PIL import Image
-from torchvision.models import resnet18
-import os
-
-# Get the directory where the current script (app.py) is located
-# Since app.py is in /app/src/ and the model is in /app/
-BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-MODEL_PATH = os.path.join(BASE_DIR, "resnet18_cifar10_finetuned.pth")
-
-# Use MODEL_PATH in your load_model function
-# Example: model.load_state_dict(torch.load(MODEL_PATH, map_location=device))
-
-# ---------------- Constants ----------------
-CIFAR10_CLASSES = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
-
-# ---------------- Model Loader ----------------
-@st.cache_resource
-def load_model():
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    
-    model = resnet18(pretrained=False)
-
-    model.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
-    model.maxpool = nn.Identity()
-    in_ftrs = model.fc.in_features
-    model.fc = nn.Sequential(
-        nn.Linear(in_ftrs, in_ftrs),
-        nn.ReLU(),
-        nn.Dropout(p=0.5),
-        nn.Linear(in_ftrs, 10)
-    )
-
-    model.load_state_dict(torch.load(MODEL_PATH, map_location=device))
-    model.to(device)
-    model.eval()
-
-    return model, device
-
-# ---------------- Preprocessing ----------------
-def preprocess_image(image):
-    transform = transforms.Compose([
-        transforms.Resize((32, 32)),
-        transforms.ToTensor(),
-        transforms.Normalize(mean=[0.4914, 0.4822, 0.4465],
-                             std=[0.2023, 0.1994, 0.2010])
-    ])
-    return transform(image).unsqueeze(0)
-
-# ---------------- UI ----------------
-st.title("🎯 CIFAR-10 Image Classifier")
-st.write("Upload an image to classify it.")
-st.write("ResNet18 model finetuned for 3 epochs with 95.6% accuracy on CIFAR10 images.")
-st.write("The model performs well on images from CIFAR10 dataset.")
-
-uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
-
-if uploaded_file:
-    try:
-        image = Image.open(uploaded_file).convert('RGB')
-
-        st.image(image, caption="Uploaded Image", width=200)
-
-        model, device = load_model()
-
-        with st.spinner("Classifying..."):
-            tensor = preprocess_image(image).to(device)
-            with torch.no_grad():
-                outputs = model(tensor)
-                probabilities = torch.softmax(outputs, dim=1)
-                confidence, predicted = torch.max(probabilities, 1)
-
-        st.success(f"Predicted: {CIFAR10_CLASSES[predicted.item()]}")
-        st.info(f"Confidence: {confidence.item()*100:.2f}%")
-
-    except Exception as e:
-        import traceback
-        st.error("An error occurred:")
-        st.text(traceback.format_exc())
-
-
-    top5_probs, top5_indices = torch.topk(probabilities, 5)
-    st.subheader("Top 5 Predictions")
-    for i in range(5):
-        label = CIFAR10_CLASSES[top5_indices[0][i].item()]
-        prob = top5_probs[0][i].item() * 100
-        st.write(f"{i+1}. {label} – {prob:.2f}%")
-
-    st.write("Done.")
-

src/streamlit_app.py

@@ -1,40 +0,0 @@
-import altair as alt
-import numpy as np
-import pandas as pd
-import streamlit as st
-
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))