metadata

title: Multimodal AI Content Understanding Platform
emoji: 🖼️
colorFrom: gray
colorTo: gray
sdk: gradio
app_file: app.py
pinned: false
license: mit
short_description: Analyze and process diverse content with multimodal AI.
sdk_version: 5.36.2

Multimodal AI Content Understanding Platform

An enterprise-grade AI platform that processes and analyzes multiple content types—images, audio, video, and text—using state-of-the-art machine learning models. This system enables cross-modal search, intelligent content understanding, automated insights extraction, and natural language Q&A across all processed content, making it a comprehensive solution for multimodal data analysis.

Overview

This platform leverages cutting-edge AI models to understand and analyze diverse content types through a unified interface. Users can upload various media formats, automatically extract meaningful information, search across different modalities using natural language, ask questions about their content, and generate comprehensive insights. The system maintains a vector database for efficient similarity search and retrieval across all content types.

Key Features

Content Processing Capabilities

Image Analysis:
- Automatic caption generation using BLIP
- Visual feature extraction with CLIP
- Content moderation and safety checks
- Dominant color extraction and sharpness analysis
- Support for JPG, PNG, GIF formats
Audio Processing:
- Speech-to-text transcription using Whisper
- Audio feature extraction (spectral analysis, tempo, pitch)
- Silence detection and dynamic range analysis
- Support for WAV, MP3 formats
Video Analysis:
- Frame-by-frame visual analysis
- Audio track extraction and transcription
- Scene change detection
- Combined multimodal insights
- Support for MP4, AVI formats
Text Understanding:
- Semantic embedding generation
- Key phrase extraction
- Content moderation
- Language analysis

Advanced Features

Cross-Modal Search: Search across all content types using natural language queries
Vector Database: ChromaDB integration for efficient similarity search
Content Moderation: Automated safety checks across all modalities
Natural Language Q&A: Ask questions about processed content with optional GPT-4 integration
Insights Generation: AI-powered analysis across multiple content items
Batch Processing: Handle multiple files with persistent storage

Technologies Used

AI Models

BLIP (Salesforce/blip-image-captioning-base): Image captioning
CLIP (openai/clip-vit-base-patch32): Vision-language understanding
Whisper (openai/whisper-base): Audio transcription
Sentence Transformers (all-MiniLM-L6-v2): Text embeddings
Toxic-BERT (unitary/toxic-bert): Content moderation

Core Technologies

Gradio: Interactive web interface
PyTorch: Deep learning framework
ChromaDB: Vector database for similarity search
OpenAI API: Optional GPT-4 integration for enhanced Q&A
Transformers: Hugging Face model library
MoviePy: Video processing
Librosa: Audio analysis
OpenCV: Computer vision operations

Running the Application

On Hugging Face Spaces

The application is deployed and ready to use at this Hugging Face Space:

Access the space through the provided URL
Upload your content files
Select the appropriate content type
Process and analyze your content
Use search, Q&A, and insights features

Local Installation

To run locally:

# Clone the repository
git clone [your-repo-url]
cd multimodal-ai-platform

# Install dependencies
pip install -r requirements.txt

# Run the application
python app.py

The application will launch at http://localhost:7860

Usage Guide

1. Content Processing

Navigate to the "Content Processing" tab
Upload a file (image, audio, video, or text)
Select the content type
Click "Process Content"
View extracted information, captions, transcripts, and metadata

2. Cross-Modal Search

Go to the "Cross-Modal Search" tab
Enter a natural language query (e.g., "find images with dogs")
Optionally filter by content type
Click "Search" to find relevant content across all modalities

3. Question & Answer

Access the "Question & Answer" tab
Enter your question about the processed content
Optionally specify content IDs to focus the search
Get AI-powered answers based on your content

4. Generate Insights

Open the "Generate Insights" tab
Enter comma-separated content IDs
Click "Generate Insights" for AI analysis
Receive patterns, relationships, and recommendations

5. Content Moderation

Use the "Content Moderation" tab
Enter text to check for safety
Receive safety scores and recommendations

Example Use Cases

Media Library Management

Automatically catalog and tag large media collections
Search across images, videos, and audio using natural descriptions
Generate metadata and descriptions for accessibility

Content Analysis & Research

Analyze video content for research purposes
Extract and search through podcast transcriptions
Cross-reference visual and textual information

Content Moderation & Safety

Automated screening of user-generated content
Multi-modal safety checks for platforms
Compliance verification for content guidelines

Educational Applications

Create searchable educational content libraries
Generate captions and transcripts for accessibility
Extract key concepts from multimedia lectures

Business Intelligence

Analyze presentation videos and extract insights
Search through meeting recordings and documents
Generate summaries from multimodal business content

API Key Configuration

While the core functionality works without an API key, adding an OpenAI API key enables:

Enhanced natural language Q&A with GPT-4
More sophisticated insight generation
Contextual understanding across content

To use these features:

Obtain an API key from OpenAI
Enter it in the API key field at the top of the interface
The key is used only for the current session

Performance Considerations

File Size Limits:
- Images: Resized to 512x512 for processing
- Audio: Limited to 300 seconds
- Video: Limited to 600 seconds
Processing Time: Varies by content type and size
GPU Acceleration: Automatically uses CUDA if available
Batch Processing: Process multiple files sequentially

Data Privacy & Storage

Content is processed locally or on your Hugging Face Space
Vector embeddings are stored in ChromaDB for search functionality
Original files are not permanently stored after processing
API keys are session-only and never logged

Troubleshooting

Common Issues:

"Model loading failed": Ensure sufficient memory/GPU resources
Slow processing: Normal for video files; consider using shorter clips
Search returns no results: Ensure content has been processed first
Transcription errors: Check audio quality and language

Performance Tips:

Use GPU-enabled spaces for faster processing
Process shorter video clips for quicker results
Batch similar content types together

Technical Architecture

The platform implements a modular architecture:

ModelManager: Handles loading and caching of AI models
ContentProcessors: Specialized processors for each modality
VectorDatabase: ChromaDB integration for similarity search
LLMHandler: Optional GPT-4 integration
GradioInterface: User interface management

Future Enhancements

Planned improvements include:

Support for additional file formats
Real-time streaming analysis
Multi-language support
Enhanced video understanding with temporal models
API endpoint for programmatic access
Export functionality for processed data

License

This project is licensed under the MIT License, allowing for both personal and commercial use with attribution.

Author

Spencer Purdy