Overview: AI Feedback on Video Submissions Module

Purpose

The AI Feedback on Video Submissions module is designed to automate the analysis of video content, focusing on tone, pacing, and clarity. It serves as a tool to enhance communication skills by providing detailed feedback, enabling users to improve their delivery in various contexts such as interviews, training sessions, and presentations.

Benefits

• Efficiency: Automates the review process, saving time compared to manual evaluations.
• Insightful Feedback: Offers detailed insights that may be overlooked in human assessments, helping users identify areas for improvement.
• Enhanced Communication: Empowers users with constructive feedback to refine their tone, pacing, and clarity, leading to more engaging communication.
• Versatility: Integrates seamlessly into applications across industries, such as e-learning platforms and customer support systems, enhancing user experiences and interactions.

Usage Scenarios

1. Recruitment Systems: Evaluate candidate responses during interviews to assess communication skills and suitability.
2. Online Learning Platforms: Provide feedback on student submissions to improve presentation and speaking abilities.
3. Customer Support Training: Help agents enhance their communication skills for more effective customer interactions.
4. Content Creation Tools: Assist creators in refining their delivery, ensuring high-quality content production.
5. Employee Performance Reviews: Analyze video submissions from employees for performance evaluations.

This module is a valuable asset for developers aiming to integrate AI-driven feedback into applications, offering practical solutions to enhance user experiences through improved communication skills.

Feature 1: Tone Analysis

This feature leverages sentiment analysis to assess the emotional tone of the speaker. By analyzing speech patterns and facial expressions, it categorizes the mood as positive, negative, or neutral, providing insights into the emotional content of video submissions.

Feature 2: Pacing Analysis

The module evaluates speaking speed and pauses, identifying areas where the speaker may be rushing or hesitating. This analysis helps in improving the flow and naturalness of communication during recordings.

Feature 3: Clarity Detection

Using audio processing techniques, this feature ensures high-quality sound by detecting and reducing background noise and enhancing speech articulation. It provides feedback on audio clarity to ensure videos are easily understandable.

Feature 4: Repetitive Content Identification

By tracking repeated phrases or topics over time, the module identifies redundant sections in video content. This helps editors streamline content by pinpointing areas that may need trimming or revising.

Feature 5: Real-Time Feedback During Recording

This feature offers instant feedback on tone, pacing, and clarity as the video is being recorded. It provides tips for immediate adjustments, enhancing the quality of live recordings through real-time guidance.

Technical Documentation for AI Feedback on Video Submissions

This module provides AI-powered feedback analysis for video submissions. It evaluates tone, pacing, and clarity in recorded responses using advanced natural language processing and speech recognition techniques.

API Endpoints

1. FastAPI Endpoint (Upload Video and Get Analysis)

from fastapi import FastAPI, UploadFile
from typing import List, Optional
import json

app = FastAPI()

@app.post("/analyze/video")
async def analyze_video(
    file: UploadFile,
    user_id: str,
    timestamp: Optional[str] = None,
    duration: Optional[int] = None
):
    """
    Analyzes tone, pacing, and clarity of a video submission.
    """
    # Process the uploaded video file here
    # Perform AI analysis
    
    return {
        "status": "success",
        "submission_id": str(uuid.uuid4()),
        "message": "Video analysis in progress. Results will be available shortly."
    }

2. FastAPI Endpoint (Get Analysis Results)

@app.get("/analysis/{submission_id}")
async def get_analysis(submission_id: str):
    """
    Retrieves the results of a previously submitted video analysis.
    """
    # Retrieve and return analysis results from storage
    
    return {
        "status": "success",
        "data": {
            "tone": 0.85,
            "pacing": 0.72,
            "clarity": 0.90,
            "confidence_score": 0.88,
            "timestamp": "2023-10-26T14:30:00Z",
            "duration": 120
        }
    }

React UI Component (Video Upload and Feedback Display)

import React, { useState } from 'react';

const VideoFeedback = () => {
    const [file, setFile] = useState(null);
    const [submissionId, setSubmissionId] = useState('');
    const [isLoading, setIsLoading] = useState(false);

    const handleUpload = async (e) => {
        e.preventDefault();
        setIsLoading(true);
        
        const formData = new FormData();
        formData.append('file', file);
        formData.append('user_id', '12345');
        formData.append('timestamp', new Date().toISOString());
        formData.append('duration', 120);

        try {
            const response = await fetch('/analyze/video', {
                method: 'POST',
                body: formData
            });

            if (!response.ok) {
                throw new Error('Upload failed');
            }

            const data = await response.json();
            setSubmissionId(data.submission_id);
            setIsLoading(false);
        } catch (error) {
            console.error('Error:', error);
            setIsLoading(false);
        }
    };

    const getFeedback = async () => {
        try {
            const response = await fetch(`/analysis/${submissionId}`);
            const data = await response.json();
            return data.data;
        } catch (error) {
            console.error('Error fetching feedback:', error);
            return null;
        }
    };

    return (
        <div>
            <h1>Video Analysis Feedback</h1>
            
            <form onSubmit={handleUpload}>
                <input
                    type="file"
                    accept="video/*"
                    onChange={(e) => setFile(e.target.files[0])}
                />
                <button type="submit">Analyze Video</button>
            </form>

            {isLoading && <p>Processing video...</p>}
            
            {submissionId && (
                <div>
                    <h2>Analysis Results for Submission ID: {submissionId}</h2>
                    <ul>
                        <li>Tone Score: 0.85</li>
                        <li>Pacing Score: 0.72</li>
                        <li>Clarity Score: 0.90</li>
                        <li>Confidence Score: 0.88</li>
                    </ul>
                </div>
            )}
        </div>
    );
};

export default VideoFeedback;

Data Schema (Pydantic Model)

from pydantic import BaseModel, Field

class AnalysisResult(BaseModel):
    tone: float = Field(..., description="Overall tone score", example=0.85)
    pacing: float = Field(..., description="Pacing score", example=0.72)
    clarity: float = Field(..., description="Clarity score", example=0.90)
    confidence_score: float = Field(..., description="Confidence in analysis", example=0.88)
    timestamp: str = Field(..., description="Timestamp of submission", example="2023-10-26T14:30:00Z")
    duration: int = Field(..., description="Video duration in seconds", example=120)

    class Config:
        json_schema_extra = {
            "example": {
                "tone": 0.85,
                "pacing": 0.72,
                "clarity": 0.90,
                "confidence_score": 0.88,
                "timestamp": "2023-10-26T14:30:00Z",
                "duration": 120
            }
        }

Usage Example

API Call from React Application:

const response = await fetch('/analysis/550e8400-e29b-41d4-a716-446655440000');
const feedbackData = await response.json();
console.log('Feedback:', feedbackData);

Example Output:

{
    "status": "success",
    "data": {
        "tone": 0.85,
        "pacing": 0.72,
        "clarity": 0.90,
        "confidence_score": 0.88,
        "timestamp": "2023-10-26T14:30:00Z",
        "duration": 120
    }
}

This module provides a complete solution for analyzing video submissions with AI-powered feedback, including both server-side processing and client-side interaction.

Technical Documentation: AI Feedback on Video Submissions Module

Module Name

AI Feedback on Video Submissions

Category

AI

Summary

The AI Feedback on Video Submissions module leverages machine learning models to analyze video content, focusing on tone, pacing, and clarity in recorded responses. It provides actionable insights for developers integrating video analysis into their applications.

Related Modules

1. Video Processing Module

   • Handles encoding, decoding, and manipulation of video files.
   • Integrates with the AI Feedback module for seamless video analysis.

2. Audio Analysis Module

   • Focuses on extracting audio features from videos.
   • Works alongside the AI Feedback module to enhance speech-related insights.

3. Computer Vision Module

   • Provides visual analysis tools, such as object detection and facial recognition.
   • Complements the AI Feedback module for non-verbal communication analysis.

4. Feedback Generation Module

   • Generates structured feedback reports based on AI analysis results.
   • Outputs actionable insights for users of the AI Feedback module.

Use Cases

1. E-Learning Platforms

   • Analyze student video submissions to provide tone and pacing feedback for assignments or presentations.

2. Video Conferencing Tools

   • Offer real-time or post-session feedback on speaker clarity and engagement during virtual meetings.

3. Corporate Training Programs

   • Evaluate employee communication skills in interviews, performance reviews, or training sessions.

4. Podcasting Platforms

   • Provide AI-generated feedback on episode tone, pacing, and clarity to improve content quality.

Integration Tips

1. Getting Started

   from ai_feedback_module import VideoAnalyzer
   
   analyzer = VideoAnalyzer(api_key="your_api_key_here")
   analysis_result = analyzer.analyze_video("path_to_your_video.mp4")
   print(analysis_result)
   

   • Use the analyze_video method to process video files.
   • Retrieve results using get_analysis with a job ID.

2. Customizing Analysis

   custom_settings = {
       "tone_weight": 0.5,
       "pacing_threshold": 120,  # Words per minute
       "clarity_score": True
   }
   analyzer.set_custom_settings(custom_settings)
   

3. Handling Edge Cases

   • Use chunking for large files with split_video to avoid memory issues.
   • Implement error handling with try-except blocks for API calls.

4. Performance Optimization

   # Optimize video processing
   analyzer.set_processing_mode("fast")
   

   • Choose between high-accuracy or fast-processing modes based on requirements.

Configuration Options

Example Workflow

# Initialize the VideoAnalyzer with your API key
from ai_feedback_module import VideoAnalyzer

analyzer = VideoAnalyzer(api_key="your_api_key")

# Analyze a video file
job_id = analyzer.submit_video("path_to_your_video.mp4")

# Poll for analysis completion
while True:
    status = analyzer.get_status(job_id)
    if status == "completed":
        result = analyzer.get_analysis(job_id)
        print(result)
        break

Conclusion

The AI Feedback on Video Submissions module empowers developers to integrate intelligent video analysis into their applications, enhancing user feedback and improving content quality. With customizable settings and robust integration options, it’s a powerful tool for various use cases in e-learning, video conferencing, and beyond.