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| import streamlit as st | |
| from transformers import AutoProcessor, Qwen2VLForConditionalGeneration | |
| from PIL import Image | |
| import torch | |
| import cv2 | |
| import tempfile | |
| from langchain import LLMChain, PromptTemplate | |
| from langchain_community.llms import Ollama | |
| from langchain_core.output_parsers import StrOutputParser | |
| # Load the processor and model directly | |
| processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct") | |
| model = Qwen2VLForConditionalGeneration.from_pretrained("Qwen/Qwen2-VL-2B-Instruct") | |
| # Check if CUDA is available and set the device accordingly | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| model.to(device) | |
| # Streamlit app | |
| st.title("Media Description Generator") | |
| uploaded_files = st.file_uploader("Choose images or videos...", type=["jpg", "jpeg", "png", "mp4", "avi", "mov"], accept_multiple_files=True) | |
| if uploaded_files: | |
| user_question = st.text_input("Ask a question about the images or videos:") | |
| if user_question: | |
| all_output_texts = [] # Initialize an empty list to store all output texts | |
| for uploaded_file in uploaded_files: | |
| file_type = uploaded_file.type.split('/')[0] | |
| if file_type == 'image': | |
| # Open the image | |
| image = Image.open(uploaded_file) | |
| # Resize image to reduce memory usage | |
| image = image.resize((512, 512)) | |
| st.image(image, caption='Uploaded Image.', use_column_width=True) | |
| st.write("Generating description...") | |
| elif file_type == 'video': | |
| # Save the uploaded video to a temporary file | |
| tfile = tempfile.NamedTemporaryFile(delete=False) | |
| tfile.write(uploaded_file.read()) | |
| # Open the video file | |
| cap = cv2.VideoCapture(tfile.name) | |
| # Extract the first frame | |
| ret, frame = cap.read() | |
| if not ret: | |
| st.error("Failed to read the video file.") | |
| continue | |
| else: | |
| # Convert the frame to an image | |
| image = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)) | |
| # Resize image to reduce memory usage | |
| image = image.resize((512, 512)) | |
| st.image(image, caption='First Frame of Uploaded Video.', use_column_width=True) | |
| st.write("Generating description...") | |
| # Release the video capture object | |
| cap.release() | |
| else: | |
| st.error("Unsupported file type.") | |
| continue | |
| # Ensure the image is loaded correctly | |
| if image is None: | |
| st.error("Failed to load the image.") | |
| continue | |
| messages = [ | |
| { | |
| "role": "user", | |
| "content": [ | |
| { | |
| "type": "image", | |
| "image": image, | |
| }, | |
| {"type": "text", "text": user_question}, | |
| ], | |
| } | |
| ] | |
| # Preparation for inference | |
| text = processor.apply_chat_template( | |
| messages, tokenize=False, add_generation_prompt=True | |
| ) | |
| # Pass the image to the processor | |
| inputs = processor( | |
| text=[text], | |
| images=[image], | |
| padding=True, | |
| return_tensors="pt", | |
| ) | |
| inputs = inputs.to(device) # Ensure inputs are on the same device as the model | |
| # Inference: Generation of the output | |
| try: | |
| generated_ids = model.generate(**inputs, max_new_tokens=512) | |
| generated_ids_trimmed = [ | |
| out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) | |
| ] | |
| output_text = processor.batch_decode( | |
| generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False | |
| ) | |
| st.write("Description:") | |
| st.write(output_text[0]) | |
| # Append the output text to the list | |
| all_output_texts.append(output_text[0]) | |
| except Exception as e: | |
| st.error(f"Error during generation: {e}") | |
| continue | |
| # Clear memory after processing each file | |
| del image, inputs, generated_ids, generated_ids_trimmed, output_text | |
| torch.cuda.empty_cache() | |
| torch.manual_seed(0) # Reset the seed to ensure reproducibility | |
| # Combine all descriptions into a single text | |
| combined_text = " ".join(all_output_texts) | |
| # Create a custom prompt | |
| custom_prompt = f"Based on the following descriptions, create a short story:\n\n{combined_text}\n\nStory:" | |
| # Define the prompt template for LangChain | |
| prompt_template = PromptTemplate( | |
| input_variables=["descriptions"], | |
| template="Based on the following descriptions, create a short story:\n\n{descriptions}\n\nStory:" | |
| ) | |
| # Create the LLMChain with the Ollama model | |
| ollama_llm = Ollama(model="llama3.1") | |
| output_parser = StrOutputParser() | |
| chain = LLMChain( | |
| llm=ollama_llm, | |
| prompt=prompt_template, | |
| output_parser=output_parser | |
| ) | |
| # Generate the story using LangChain | |
| story = chain.run({"descriptions": combined_text}) | |
| # Display the generated story | |
| st.write("Generated Story:") | |
| st.write(story) |