Create app.py

app.py

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+import streamlit as st
+import os
+import cv2
+import tempfile
+from PIL import Image
+from transformers import VisionEncoderDecoderModel, ViTFeatureExtractor, AutoTokenizer, pipeline
+import torch
+import pandas as pd
+from nltk.corpus import wordnet
+import nltk
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+nltk.download('wordnet')
+nltk.download('omw-1.4')
+
+# Load the pre-trained model for image captioning
+model_name = "NourFakih/Vit-GPT2-COCO2017Flickr-85k-09"
+model = VisionEncoderDecoderModel.from_pretrained(model_name)
+feature_extractor = ViTFeatureExtractor.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+
+model_sum_name = "google-t5/t5-base"
+tokenizer_sum = AutoTokenizer.from_pretrained("google-t5/t5-base")
+model_sum = AutoModelForSeq2SeqLM.from_pretrained("google-t5/t5-base")
+# Initialize the summarization model
+summarize_pipe = pipeline("summarization", model=model_sum_name)
+
+def generate_caption(image):
+    pixel_values = feature_extractor(images=image, return_tensors="pt").pixel_values
+    output_ids = model.generate(pixel_values)
+    caption = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+    return caption
+
+def get_synonyms(word):
+    synonyms = set()
+    for syn in wordnet.synsets(word):
+        for lemma in syn.lemmas():
+            synonyms.add(lemma.name())
+    return synonyms
+
+def search_captions(query, captions):
+    query_words = query.split()
+    query_synonyms = set(query_words)
+    for word in query_words:
+        query_synonyms.update(get_synonyms(word))
+    
+    results = []
+    for path, caption in captions.items():
+        if any(word in caption.split() for word in query_synonyms):
+            results.append((path, caption))
+    
+    return results
+
+def convert_frame_to_pil(frame):
+    return Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
+
+def process_video(video_path, frame_interval):
+    cap = cv2.VideoCapture(video_path)
+    if not cap.isOpened():
+        st.error("Error: Could not open video file.")
+        return [], pd.DataFrame()
+    
+    video_length = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) - 1
+    frames = []
+    count = 0
+    frame_id = 0
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+        if count % frame_interval == 0:
+            frames.append((frame_id, frame))
+            frame_id += 1
+        count += 1
+        if count > video_length - 1:
+            break
+    cap.release()
+    
+    captions_data = []
+    for i, (frame_id, frame) in enumerate(frames):
+        pil_image = convert_frame_to_pil(frame)
+        caption = generate_caption(pil_image)
+        captions_data.append({'Frame_ID': frame_id + 1, 'Caption': caption})
+    
+    captions_df = pd.DataFrame(captions_data)
+    return frames, captions_df
+
+def image_captioning_page():
+    st.title("Image Gallery with Captioning and Search")
+
+    # Sidebar for search functionality
+    with st.sidebar:
+        query = st.text_input("Search images by caption:")
+
+    # Right side for folder path input and displaying images
+    folder_path = st.text_input("Enter the folder path containing images:")
+    
+    if folder_path and os.path.isdir(folder_path):
+        image_files = [f for f in os.listdir(folder_path) if f.lower().endswith(('png', 'jpg', 'jpeg'))]
+        captions = {}
+
+        for image_file in image_files:
+            image_path = os.path.join(folder_path, image_file)
+            image = Image.open(image_path)
+            caption = generate_caption(image)
+            captions[image_path] = caption
+
+        # Display images in a 4-column grid
+        cols = st.columns(4)
+        for idx, (image_path, caption) in enumerate(captions.items()):
+            with cols[idx % 4]:
+                st.image(image_path, caption=caption)
+
+        if query:
+            results = search_captions(query, captions)
+            st.write("Search Results:")
+            for image_path, caption in results:
+                st.image(image_path, caption=caption)
+
+        # Save captions to CSV
+        if st.button("Save captions to excel"):
+            df = pd.DataFrame(list(captions.items()), columns=['Image', 'Caption'])
+            save_path = st.text_input("Enter the path to save the Excel file:", folder_path)
+            if save_path:
+                if not os.path.exists(save_path):
+                    os.makedirs(save_path)
+                excel_file_path = os.path.join(save_path, "captions.xlsx")
+                df.to_excel(excel_file_path, index=False)
+                st.success(f"Captions saved to {excel_file_path}")
+
+def live_camera_captioning_page():
+    st.title("Live Captioning with Webcam")
+    run = st.checkbox('Run')
+    FRAME_WINDOW = st.image([])
+
+    camera = cv2.VideoCapture(0)
+
+    while run:
+        ret, frame = camera.read()
+        if not ret:
+            st.write("Failed to capture image.")
+            break
+        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        FRAME_WINDOW.image(frame)
+        pil_image = Image.fromarray(frame)
+        caption = generate_caption(pil_image)
+        st.write("Caption: ", caption)
+        cv2.waitKey(500)  # Capture an image every 0.5 seconds
+
+    camera.release()
+
+def video_captioning_page():
+    st.title("Video Captioning")
+    
+    # Sidebar for search functionality
+    with st.sidebar:
+        query = st.text_input("Search videos by caption:")
+
+    # Right side for folder path input and displaying videos
+    folder_path = st.text_input("Enter the folder path containing videos:")
+    
+    if folder_path and os.path.isdir(folder_path):
+        video_files = [f for f in os.listdir(folder_path) if f.lower().endswith(('mp4', 'avi', 'mov', 'mkv'))]
+        captions = {}
+
+        for video_file in video_files:
+            video_path = os.path.join(folder_path, video_file)
+            frames, captions_df = process_video(video_path, frame_interval=20)
+            
+            if frames and not captions_df.empty:
+                generated_captions = ' '.join(captions_df['Caption'])
+                summary = summarize_pipe(generated_captions)[0]['summary_text']
+                captions[video_path] = summary
+
+        # Display videos in a 4-column grid
+        cols = st.columns(4)
+        for idx, (video_path, summary) in enumerate(captions.items()):
+            with cols[idx % 4]:
+                st.video(video_path, caption=summary)
+
+        if query:
+            results = search_captions(query, captions)
+            st.write("Search Results:")
+            for video_path, summary in results:
+                st.video(video_path, caption=summary)
+
+        # Save captions to CSV
+        if st.button("Save captions to excel"):
+            df = pd.DataFrame(list(captions.items()), columns=['Video', 'Caption'])
+            save_path = st.text_input("Enter the path to save the Excel file:", folder_path)
+            if save_path:
+                if not os.path.exists(save_path):
+                    os.makedirs(save_path)
+                excel_file_path = os.path.join(save_path, "captions.xlsx")
+                df.to_excel(excel_file_path, index=False)
+                st.success(f"Captions saved to {excel_file_path}")
+
+def main():
+    st.sidebar.title("Navigation")
+    page = st.sidebar.selectbox("Select a page", ["Image Captioning", "Live Camera Captioning", "Video Captioning"])
+
+    if page == "Image Captioning":
+        image_captioning_page()
+    elif page == "Live Camera Captioning":
+        live_camera_captioning_page()
+    elif page == "Video Captioning":
+        video_captioning_page()
+
+if __name__ == "__main__":
+    main()