Delete app.py

app.py

@@ -1,97 +0,0 @@
-import streamlit as st
-from PIL import Image
-import cv2
-import numpy as np
-import subprocess
-import shutil
-import os
-import torch
-import time
-
-
-
-
-def clear_detect_directory():
-    detect_directory = "yolov5/runs/detect"
-    if os.path.exists(detect_directory):
-        shutil.rmtree(detect_directory)
-    os.makedirs(detect_directory)
-
-
-def save_image():
-    
-    st.title("Hand Sign Detection")
-    col1, col2 = st.columns(2)  # 2 for two col
-    pd_df=None
-    with col1:
-        genre = st.radio(
-            "Upload Your Hand Sign",
-            ('Browse', 'Camera'))
-    
-        if genre == 'Camera':
-            uploaded_image = st.camera_input("Take a picture")
-            
-        else:
-            uploaded_image = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
-    
-        if uploaded_image is not None:
-            
-            # Convert the image to a format compatible with PIL and OpenCV
-            pil_image = Image.open(uploaded_image)
-            opencv_image = np.array(pil_image)
-            opencv_image = cv2.cvtColor(opencv_image, cv2.COLOR_BGR2RGB)
-    
-            # Provide a file path to save the image
-            upload_image_path = "processed_image.jpg"
-            cv2.imwrite(upload_image_path, opencv_image)
-    
-            st.success(f"Image saved as {upload_image_path}")
-            st.success("Processing Image...")
-
-            # label of image:
-            model = torch.hub.load('ultralytics/yolov5', 'custom', path='best.pt')  # local model
-            results = model(upload_image_path)
-            pd_df = (results.pandas().xyxy[0])
-            
-               # max_confidence_name = pd_df.loc[pd_df['confidence'].idxmax(), 'name']
-            
-            clear_detect_directory()
-            
-            command = [
-                "python",
-                "yolov5/detect.py",
-                "--weights", "best.pt",
-                "--img", "416",
-                "--conf", "0.50",
-                "--source", upload_image_path
-            ]
-    
-            process = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
-            std_out, std_err = process.communicate()
-            if process.returncode != 0:
-                error_message = f"Error: {std_err}"
-                st.text(error_message)
-
-            
-    
-            
-    with col2:
-        detect_image_pred = "yolov5/runs/detect/exp/processed_image.jpg"
-        if os.path.exists(detect_image_pred):
-            st.text("Detected Gesture")
-            st.image(detect_image_pred, caption="Detected Image", use_column_width=True)
-    
-            st.text("Detection class probabilities")
-            if pd_df is not None:
-                st.text(pd_df)
-            else:
-                pass
-        else:
-            st.text("Detection Threshold is 60")
-    st.text("Detection Gesture")
-    st.text("Note: Make clean Gesture if not detected try another Gesture")
-    st.image("Untitled_img.png")
-
-
-
-save_image()