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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 | |
| # Step 1: Load the model | |
| def load_model(): | |
| st.write("Loading the model...") | |
| processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct") | |
| model = Qwen2VLForConditionalGeneration.from_pretrained("Qwen/Qwen2-VL-2B-Instruct") | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| model.to(device) | |
| st.write("Model loaded successfully!") | |
| return processor, model, device | |
| # Step 2: Upload image or video | |
| def upload_media(): | |
| return st.file_uploader("Choose images or videos...", type=["jpg", "jpeg", "png", "mp4", "avi", "mov"], accept_multiple_files=True) | |
| # Step 3: Enter your question | |
| def get_user_question(): | |
| return st.text_input("Ask a question about the images or videos:") | |
| # Process image | |
| def process_image(uploaded_file): | |
| image = Image.open(uploaded_file) | |
| image = image.resize((256,256)) # Reduce size to save memory | |
| st.image(image, caption='Uploaded Image.', use_column_width=True) | |
| return image | |
| # Process video | |
| def process_video(uploaded_file): | |
| tfile = tempfile.NamedTemporaryFile(delete=False) | |
| tfile.write(uploaded_file.read()) | |
| cap = cv2.VideoCapture(tfile.name) | |
| ret, frame = cap.read() | |
| cap.release() | |
| if not ret: | |
| st.error("Failed to read the video file.") | |
| return None | |
| image = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)) | |
| image = image.resize((256, 256)) # Reduce size to save memory | |
| st.image(image, caption='First Frame of Uploaded Video.', use_column_width=True) | |
| return image | |
| # Generate description | |
| def generate_description(processor, model, device, image, user_question): | |
| messages = [ | |
| { | |
| "role": "user", | |
| "content": [ | |
| {"type": "image", "image": image}, | |
| {"type": "text", "text": user_question}, | |
| ], | |
| } | |
| ] | |
| text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) | |
| inputs = processor(text=[text], images=[image], padding=True, return_tensors="pt").to(device) | |
| 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) | |
| return output_text[0] | |
| # Generate story | |
| def generate_story(descriptions): | |
| combined_text = " ".join(descriptions) | |
| prompt_template = PromptTemplate( | |
| input_variables=["descriptions"], | |
| template="Based on the following descriptions, create a short story:\n\n{descriptions}\n\nStory:" | |
| ) | |
| ollama_llm = Ollama(model="llama3.1") | |
| output_parser = StrOutputParser() | |
| chain = LLMChain(llm=ollama_llm, prompt=prompt_template, output_parser=output_parser) | |
| return chain.run({"descriptions": combined_text}) | |
| # Main function to control the flow | |
| def main(): | |
| st.title("Media Story Generator") | |
| # Step 1: Load the model | |
| processor, model, device = load_model() | |
| # Step 2: Upload image or video | |
| uploaded_files = upload_media() | |
| if uploaded_files: | |
| # Step 3: Enter your question | |
| user_question = get_user_question() | |
| if user_question: | |
| # Step 4: Generate description | |
| st.write("Step 4: Generate description") | |
| generate_description_button = st.button("Generate Descriptions", key="generate_descriptions") | |
| if generate_description_button: | |
| all_output_texts = [] | |
| for idx, uploaded_file in enumerate(uploaded_files): | |
| file_type = uploaded_file.type.split('/')[0] | |
| image = None | |
| if file_type == 'image': | |
| image = process_image(uploaded_file) | |
| elif file_type == 'video': | |
| image = process_video(uploaded_file) | |
| else: | |
| st.error("Unsupported file type.") | |
| continue | |
| if image: | |
| description = generate_description(processor, model, device, image, user_question) | |
| st.write(f"Description for file {idx + 1}:") | |
| st.write(description) | |
| all_output_texts.append(description) | |
| # Store descriptions in session state | |
| st.session_state["all_output_texts"] = all_output_texts | |
| # Check if descriptions are available in session state | |
| if "all_output_texts" in st.session_state and st.session_state["all_output_texts"]: | |
| st.write("Generate story") | |
| generate_story_button = st.button("Generate Story", key="generate_story") | |
| if generate_story_button: | |
| story = generate_story(st.session_state["all_output_texts"]) | |
| st.write("Generated Story:") | |
| st.write(story) | |
| if __name__ == "__main__": | |
| main() |