Create app.py
Browse files
app.py
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#from google.colab import userdata
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import os
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import base64
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import json
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import cv2
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import moviepy.editor as mp
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import gradio as gr
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from pathlib import Path
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from llama_index.core import Settings
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from llama_index.core import StorageContext
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from llama_index.core import SimpleDirectoryReader
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from llama_index.core.indices.multi_modal.base import MultiModalVectorStoreIndex
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from llama_index.embeddings.mistralai import MistralAIEmbedding
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from llama_index.vector_stores.milvus import MilvusVectorStore
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# Configure default embedding model
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Settings.embed_model = MistralAIEmbedding(
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"mistral-embed",
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api_key=os.getenv('MISTRAL_API_KEY')
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)
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# Global variables for session state
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index = None
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metadata = None
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# Functions for video and audio processing
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def process_video(video_path, output_folder, output_audio_path):
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Path(output_folder).mkdir(parents=True, exist_ok=True)
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video_to_images(video_path, output_folder)
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video_to_audio(video_path, output_audio_path)
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with open(os.path.join(output_folder, "output_text.txt"), "w") as file:
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file.write(audio_to_text(output_audio_path))
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os.remove(output_audio_path)
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# breakpoint()
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return {"Video path": video_path, "Audio path": output_audio_path, "Text": audio_to_text(output_audio_path)}
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def video_to_images(video_path, output_folder, frame_interval=30):
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cap = cv2.VideoCapture(video_path)
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frame_count = 0
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while cap.isOpened():
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ret, frame = cap.read()
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if not ret:
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break
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if frame_count % frame_interval == 0:
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cv2.imwrite(f"{output_folder}/frame_{frame_count}.jpg", frame)
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frame_count += 1
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cap.release()
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def audio_to_text(audio_path):
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from openai import OpenAI
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client = OpenAI(api_key=os.getenv('OPENAI_API_KEY'))
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with open(audio_path, "rb") as audio_file:
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transcript = client.audio.transcriptions.create(
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model="whisper-1",
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file=audio_file
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)
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return transcript.text
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def video_to_audio(video_path, output_path):
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video = mp.VideoFileClip(video_path)
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video.audio.write_audiofile(output_path)
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def create_index(output_folder):
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text_store = MilvusVectorStore(
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uri="milvus_local.db",
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collection_name="text_collection",
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overwrite=True,
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dim=1024
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)
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image_store = MilvusVectorStore(
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uri="milvus_local.db",
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collection_name="image_collection",
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overwrite=True,
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dim=512
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)
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storage_context = StorageContext.from_defaults(
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vector_store=text_store,
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image_store=image_store
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)
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documents = SimpleDirectoryReader(output_folder).load_data()
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return MultiModalVectorStoreIndex.from_documents(
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documents,
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storage_context=storage_context
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)
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# Gradio callbacks
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def process_video_callback(video_file):
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global index, metadata
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# breakpoint()
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output_folder = "output"
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output_audio_path = "output/audio.wav"
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video_path = video_file.name
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# Process video and create index
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metadata = process_video(video_path, output_folder, output_audio_path)
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# breakpoint()
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index = create_index(output_folder)
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return "Video processed successfully!"
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def query_video_callback(query):
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global index, metadata
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if not index:
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return "No video index found. Please upload and process a video first."
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# Retrieve relevant context from the index
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# breakpoint()
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retrieval_result = index.as_retriever().retrieve(query)
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text_contexts = []
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image_documents = []
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for node in retrieval_result:
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if hasattr(node.node, 'image'):
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image_documents.append(node.node)
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else:
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text_contexts.append(node.node.text)
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# Combine text contexts
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context_str = "\n".join(text_contexts)
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metadata_str = json.dumps(metadata, indent=2)
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# Generate response
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if image_documents:
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response = f"Text Context: {context_str}\nMetadata: {metadata_str}\nImage Documents Found: {len(image_documents)}"
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else:
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response = "No relevant images found to answer the query."
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return response
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# Gradio Interface
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with gr.Blocks() as demo:
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gr.Markdown("## Multi-Modal RAG with Gradio")
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video_input = gr.File(label="Upload a Video", file_types=[".mp4", ".avi"])
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process_button = gr.Button("Process Video")
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query_input = gr.Textbox(label="Ask a Question About the Video")
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query_button = gr.Button("Submit Query")
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output_text = gr.Textbox(label="Response")
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process_button.click(process_video_callback, inputs=video_input, outputs=output_text)
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query_button.click(query_video_callback, inputs=query_input, outputs=output_text)
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demo.launch(debug=True)
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