Create app.py

app.py

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+import streamlit as st
+import os
+from PIL import Image, ImageDraw, ImageFont
+import cv2
+import tempfile
+import time  # For simulating processing time
+from object_detection import detectObjects
+from object_detection import detectVideo
+from object_detection_count import detectObjectsAndCount
+from pose_analysis import process_gif
+from traffic_sign_detection import detectObjects
+
+# Constants
+MAX_FILE_SIZE_MB = 250
+TABS = ["Object Detection", "Pose Analysis", "Object Counting", "Traffic Sign Detection"]
+
+# Helper function to check file size
+def check_file_size(file):
+    file.seek(0, os.SEEK_END)
+    file_size = file.tell() / (1024 * 1024)  # Convert to MB
+    file.seek(0)  # Reset file pointer
+    return file_size
+
+# Placeholder function for processing logic
+def process_file(uploaded_file, tab_name, confidence_score, progress_placeholder, class_type):
+    progress_placeholder.info(f"Processing... Please wait. (Confidence Score: {confidence_score})")
+    time.sleep(2)  # Simulate processing delay
+    if tab_name == 'Object Detection':
+        # Process Image
+        if uploaded_file.name.endswith((".jpg", ".png", ".jpeg")):  # Image file
+            #img = Image.open(uploaded_file)
+            progress_placeholder.empty()  # Clear the "Processing..." message
+            img = detectObjects(uploaded_file.name, confidence_score)
+            return img, "image"
+    
+        # Process Video
+        elif uploaded_file.name.endswith((".mp4", ".avi", ".mov", ".gif")):  # Video file
+            #temp_video_path = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4").name
+            #with open(temp_video_path, "wb") as f:
+             #   f.write(uploaded_file.read())
+            progress_placeholder.empty()  # Clear the "Processing..." message
+            temp_video_path = detectVideo(uploaded_file.name, confidence_score)
+            return temp_video_path, "video"
+    
+        # Unsupported file type
+        else:
+            progress_placeholder.empty()  # Clear the "Processing..." message
+            st.error("Unsupported file format! Please upload an image or video.")
+            return None, None
+    elif tab_name == 'Object Counting':
+         # Process Image
+        if uploaded_file.name.endswith((".jpg", ".png", ".jpeg")):  # Image file
+            #img = Image.open(uploaded_file)
+            progress_placeholder.empty()  # Clear the "Processing..." message
+            img, count = detectObjectsAndCount(uploaded_file.name, confidence_score, class_type)
+            return img, "image"
+    
+        # Process Video
+        elif uploaded_file.name.endswith((".mp4", ".avi", ".mov", ".gif")):  # Video file
+            #temp_video_path = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4").name
+            #with open(temp_video_path, "wb") as f:
+             #   f.write(uploaded_file.read())
+            progress_placeholder.empty()  # Clear the "Processing..." message
+            temp_video_path = detectVideo(uploaded_file.name, confidence_score)
+            return temp_video_path, "video"
+    
+        # Unsupported file type
+        else:
+            progress_placeholder.empty()  # Clear the "Processing..." message
+            st.error("Unsupported file format! Please upload an image or video.")
+            return None, None       
+    elif tab_name == 'Pose Analysis':
+            progress_placeholder.empty()  # Clear the "Processing..." message
+            temp_video_path = process_gif(uploaded_file.name, confidence_score)
+            return temp_video_path, "video"        
+    elif tab_name == 'Traffic Sign Detection':
+        if uploaded_file.name.endswith((".jpg", ".png", ".jpeg")):  # Image file
+            #img = Image.open(uploaded_file)
+            progress_placeholder.empty()  # Clear the "Processing..." message
+            img = detectObjects(uploaded_file.name, confidence_score)
+            return img, "image"
+
+# Streamlit app layout
+st.title("AI Video/Image Analysis Platform")
+st.write("Upload an image or video and choose a tab for analysis.")
+
+# Tabs for different functionalities
+tab = st.tabs(TABS)
+
+uploaded_file = None
+
+for i, tab_name in enumerate(TABS):
+    with tab[i]:
+        st.header(tab_name)
+
+        # File uploader
+        uploaded_file = st.file_uploader(
+            "Upload an Image/Video", type=["jpg", "jpeg", "png", "gif", "mp4", "avi", "mov"], key=tab_name
+        )
+
+        # Check file size
+        if uploaded_file:
+            file_size = check_file_size(uploaded_file)
+            if file_size > MAX_FILE_SIZE_MB:
+                st.error(f"File size exceeds {MAX_FILE_SIZE_MB} MB. Please upload a smaller file.")
+            else:
+                st.success(f"Uploaded file: {uploaded_file.name} ({file_size:.2f} MB)")
+
+                # Confidence score input
+                confidence_score = st.number_input(
+                    "Adjust Confidence Score",
+                    min_value=0.0,
+                    max_value=1.0,
+                    value=0.5,
+                    step=0.01,
+                    help="Set the confidence score threshold for the analysis (default: 0.5).",
+                    key=f"confidence_{tab_name}",
+                )
+
+                # Additional input for "Object Counting" tab
+                class_type = None
+                if tab_name == "Object Counting":
+                    class_type = st.text_input(
+                        "Enter Class Type",
+                        value="car",  # Default value, adjust as needed
+                        help="Specify the class type to count (e.g., 'car', 'person').",
+                        key=f"class_type_{tab_name}",
+                    )
+
+                # Process file when button is clicked
+                if st.button(f"Process {tab_name}", key=f"process_{tab_name}"):
+                    # Placeholder for the processing message
+                    progress_placeholder = st.empty()
+                    with st.spinner("Processing... Please wait."):
+                        result, result_type = process_file(
+                            uploaded_file,
+                            tab_name,
+                            confidence_score,
+                            progress_placeholder,
+                            class_type,  # Pass class_type to the processing function
+                        )
+                        if result_type == "video":
+                            if result:
+                                st.success(f"{tab_name} completed successfully!")
+                                st.video(result)
+                        if result_type == "image":
+                            #if result:
+                                st.success(f"{tab_name} completed successfully!")
+                                st.image(result, caption=f"{tab_name} Result", use_column_width=True)