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| import gradio as gr | |
| from PIL import Image | |
| import torch | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| import cv2 | |
| import numpy as np | |
| # # Ensure GPU usage if available | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| # Initialize the model and tokenizer | |
| model = AutoModelForCausalLM.from_pretrained("ManishThota/SparrowVQE", | |
| torch_dtype=torch.float16, | |
| device_map="auto", | |
| trust_remote_code=True) | |
| tokenizer = AutoTokenizer.from_pretrained("ManishThota/SparrowVQE", trust_remote_code=True) | |
| def video_to_frames(video, fps=1): | |
| """Converts a video file into frames and stores them as PNG images in a list.""" | |
| frames_png = [] | |
| cap = cv2.VideoCapture(video) | |
| if not cap.isOpened(): | |
| print("Error opening video file") | |
| return frames_png | |
| frame_count = 0 | |
| frame_interval = int(cap.get(cv2.CAP_PROP_FPS)) // fps # Calculate frame interval | |
| while cap.isOpened(): | |
| ret, frame = cap.read() | |
| if not ret: | |
| print("Can't receive frame (stream end?). Exiting ...") | |
| break | |
| if frame_count % frame_interval == 0: | |
| is_success, buffer = cv2.imencode(".png", frame) | |
| if is_success: | |
| frames_png.append(np.array(buffer).tobytes()) | |
| frame_count += 1 | |
| cap.release() | |
| return frames_png | |
| def extract_frames(frame): | |
| # Convert binary data to a numpy array | |
| frame_np = np.frombuffer(frame, dtype=np.uint8) | |
| # Decode the PNG image | |
| image_rgb = cv2.imdecode(frame_np, flags=cv2.IMREAD_COLOR) # Assuming it's in RGB format | |
| # Convert RGB to BGR | |
| image_bgr = cv2.cvtColor(image_rgb, cv2.COLOR_RGB2BGR) | |
| return image_bgr | |
| def predict_answer(image, video, question, max_tokens=100): | |
| text = f"A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\n{question}? ASSISTANT:" | |
| input_ids = tokenizer(text, return_tensors='pt').input_ids.to(device) | |
| if image: | |
| # Process as an image | |
| image = image.convert("RGB") | |
| image_tensor = model.image_preprocess(image) | |
| #Generate the answer | |
| output_ids = model.generate( | |
| input_ids, | |
| max_new_tokens=max_tokens, | |
| images=image_tensor, | |
| use_cache=True)[0] | |
| return tokenizer.decode(output_ids[input_ids.shape[1]:], skip_special_tokens=True).strip() | |
| elif video: | |
| # Process as a video | |
| frames = video_to_frames(video) | |
| answers = [] | |
| for frame in frames: | |
| image = extract_frames(frame) | |
| image_tensor = model.image_preprocess(image) | |
| # Generate the answer | |
| output_ids = model.generate( | |
| input_ids, | |
| max_new_tokens=max_tokens, | |
| images=image_tensor, | |
| use_cache=True)[0] | |
| answer = tokenizer.decode(output_ids[input_ids.shape[1]:], skip_special_tokens=True).strip() | |
| answers.append(answer) | |
| return answers | |
| else: | |
| return "Unsupported file type. Please upload an image or video." | |
| def gradio_predict(image, video, question, max_tokens): | |
| answer = predict_answer(image, video, question, max_tokens) | |
| return answer | |
| iface = gr.Interface( | |
| fn=gradio_predict, | |
| inputs=[ | |
| gr.Image(type="pil", label="Upload or Drag an Image"), | |
| gr.Video(label="Upload your video here"), | |
| gr.Textbox(label="Question", placeholder="e.g. Can you explain the slide?", scale=4), | |
| gr.Slider(2, 500, value=25, label="Token Count", info="Choose between 2 and 500")], | |
| outputs=gr.TextArea(label="Answer"), | |
| # outputs=gr.Image(label="Output"), | |
| title="Video/Image Viewer", | |
| description="Upload an image or video to view it or extract frames from the video.", | |
| ) | |
| iface.launch(debug=True) |