Update app.py

app.py

@@ -1,24 +1,6 @@
 import streamlit as st
 
-import requests
-from PIL import Image
-from io import BytesIO
-from transformers import (
-    AutoModelForImageClassification,
-    AutoFeatureExtractor,
-    AutoConfig,
-)
-from torchcam.methods import GradCAM
-from torchcam.utils import overlay_mask
-import matplotlib.pyplot as plt
-from torchvision.transforms.functional import to_pil_image
-from torchcam import methods
-
-# TODO I have an error with those
-# CAM_METHODS = ["CAM", "GradCAM", "GradCAMpp", "SmoothGradCAMpp", "ScoreCAM", "SSCAM", "ISCAM", "XGradCAM", "LayerCAM"]
-CAM_METHODS = ["CAM", "GradCAM", "GradCAMpp", "LayerCAM"]
-
-SUPPORTED_MODELS = ["convnext"]
+import sys
 
 
 def main():
@@ -26,105 +8,7 @@ def main():
     st.set_page_config(layout="wide")
 
     # Designing the interface
-    st.title("TorchCAM 📸 and Transformers 🤗")
-    st.header("Class activation explorer")
-    # For newline
-    st.write("\n")
-    st.write("`torch-cam`: https://github.com/frgfm/torch-cam")
-    st.write("`transformers`: https://github.com/huggingface/transformers")
-    st.write("Upload an image, select your CAM method and hit the Compute Cam button!")
-
-    # For newline
-    st.write("\n")
-    # Set the columns
-    cols = st.columns((1, 1))
-    cols[0].header("Input image")
-    cols[1].header("Overlayed CAM")
-    # Sidebar
-    # File selection
-    st.sidebar.title("Input selection")
-    # Disabling warning
-    st.set_option("deprecation.showfileUploaderEncoding", False)
-    # Choose your own image
-    uploaded_file = st.sidebar.file_uploader(
-        "Upload files", type=["png", "jpeg", "jpg"]
-    )
-    if uploaded_file is not None:
-        img = Image.open(BytesIO(uploaded_file.read()), mode="r").convert("RGB")
-    else:
-        r = requests.get(
-            "https://i.insider.com/5df126b679d7570ad2044f3e?width=700&format=jpeg&auto=webp"
-        )
-        img = Image.open(BytesIO(r.content))
-    cols[0].image(img, use_column_width=True)
-
-    model_name = st.sidebar.text_input("Model name", "facebook/convnext-tiny-224")
-
-    if model_name is not None:
-        with st.spinner("Loading model..."):
-            config = AutoConfig.from_pretrained(model_name)
-            model_type = config.model_type
-            if model_type not in SUPPORTED_MODELS:
-                st.warning(
-                    f"{model_type} not in supported models: {','.join(SUPPORTED_MODELS)}"
-                )
-            else:
-                feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)
-                model = AutoModelForImageClassification.from_pretrained(model_name)
-
-    cam_method = st.sidebar.selectbox("CAM method", CAM_METHODS)
-    if cam_method is not None:
-        cam_extractor = methods.__dict__[cam_method](
-            model, target_layer=model.convnext.encoder.stages[-1].layers[-1]
-        )
-
-    # label choices
-    class_choices = [
-        f"{idx + 1} - {class_name}" for idx, class_name in model.config.id2label.items()
-    ]
-    class_selection = st.sidebar.selectbox(
-        "Class selection", ["Predicted class (argmax)"] + class_choices
-    )
-    # for newline
-    st.sidebar.write("\n")
-
-    if st.sidebar.button("Compute CAM"):
-        # compute cam
-        if img is None:
-            st.sidebar.error("Please upload an image first")
-        else:
-            with st.spinner("Analyzing..."):
-                # Set your CAM extractor
-                cam_extractor = GradCAM(
-                    model, target_layer=model.convnext.encoder.stages[-1].layers[-1]
-                )
-                inputs = feature_extractor(img, return_tensors="pt")
-                logits = model(**inputs).logits
-                # select the target class
-                if class_selection == "Predicted class (argmax)":
-                    class_idx = logits.squeeze(0).argmax().item()
-                else:
-                    class_idx = model.config.label2id[
-                        class_selection.rpartition(" - ")[-1]
-                    ]
-                print(class_idx)
-                # run the cam extractor
-                cams = cam_extractor(class_idx, logits)
-                cam = cams[0] if len(cams) == 1 else cam_extractor.fuse_cams(cams)
-                # resize + overlay
-                result = overlay_mask(img, to_pil_image(cam, mode="F"), alpha=0.5)
-                # display it
-                fig, ax = plt.subplots()
-                result = overlay_mask(img, to_pil_image(cam, mode="F"), alpha=0.5)
-                ax.imshow(result)
-                ax.axis("off")
-                cols[1].pyplot(fig)
-                if class_selection == "Predicted class (argmax)":
-                    # show the predicted class
-                    st.markdown(
-                        f"<p style='text align: center'> Predicted class is {config.id2label[class_idx]}</p>",
-                        unsafe_allow_html=True,
-                    )
-
+    st.title("sys.version)")
+    
 
 main()