💬 Talkwise-AI

This is a self-contained NLP-powered web app that analyzes conversation transcripts for sentiment and filler-word usage. Built using Hugging Face Transformers, spaCy, and Streamlit. The app extracts per-turn and overall conversation insights such as sentiment, filler word usage, and speaker statistics, and presents them in an engaging dashboard.

💼 Why This App Is Useful

📌 Business Use Case

In any communication-heavy domain—like customer support, team meetings, interviews, or sales conversations—understanding how people speak can be as important as what they say.

This app provides structured insights into:

• Emotional tone of a dialogue
• Clarity and fluency (via filler usage)
• Speaker-specific tendencies

🎯 Who Benefits from This?

• Customer Support Teams - Monitor call quality, emotional tone, overuse of fillers
• Sales Teams - Analyze winning vs. losing call patterns
• UX Researchers / Analysts - Study conversation clarity during usability tests
• Interviewers / Hiring Panels - Track candidate fluency, confidence, or tone
• Educators / Public Speakers - Get feedback on speaking style

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📊 Metrics & Visualizations in the Dashboard

The application analyzes the conversation transcript and presents the following metrics in the Streamlit dashboard:

🖥️ UI Components

📤 Sidebar

• Upload .txt file containing the transcript.

🏠 Landing Summary

• 👥 Speakers Detected
• 📄 Total Turns
• 💬 Average Filler Ratio
• 📊 Average Sentiment Polarity

📋 Annotated Transcript

• Full parsed dialogue with computed metrics per turn.

🔽 Per-Turn Metrics (Expandable)

• Sentiment Trend (line chart)
• Filler Ratio (bar chart)
• Colored Sentiment Line (speaker overlay)

🔽 Overall Metrics (Expandable)

• Speaker Summary Table
• Sentiment Distribution (Pie)
• Word Cloud
• Top 5 Filler Words (Bar/Table toggle)

Folder Structure

.
├── app/                         # Core NLP logic
│   ├── analyzer.py              # Sentiment & filler ratio computations
│   ├── metrics.py               # Summary & per-turn metric calculations
│   └── parser.py                # Parses raw transcript string
│
├── config/                      # YAML config files
│   └── pipeline_config.yaml     # Filler word list, top filler count, etc.
│
├── data/                        # Transcript input/output storage
│   └── transcript.txt           # Sample input transcript file
│
├── tests/                       # Unit tests
│   ├── test_parser.py
│   ├── test_analyzer.py
│   └── test_metrics.py
│
├── ui/                          # Streamlit dashboard
│   ├── dashboard.py             # Main Streamlit entry logic
│   ├── components.py            # Shared styles (card CSS)
│   └── sections/                # Modular UI views
│       ├── landing_summary.py
│       ├── annotated_transcript.py
│       ├── per_turn_metrics.py
│       └── overall_metrics.py
│
├── utils/                       # Helper functions
│   └── common_functions.py      # YAML loader and shared utilities
│
├── app.py                       # ENTRY POINT for Streamlit app
├── requirements.txt             # Python dependencies
├── setup.py                     # Project metadata
└── README.md                    

⚙️ Setup Instructions

1. Clone the repository

git clone https://github.com/your-username/talkwise-ai.git
cd talkwise-ai

2. Create a virtual environment

python -m venv venv
source venv/bin/activate  # or venv\Scripts\activate on Windows

3. Install the dependencies

pip install -r requirements.txt

4. Run Unit Tests (Optional)

pytest tests/

5. Run the Application

streamlit run app.py

🖼️ Screenshots

1. Transcript Upload Area

2. Landing Summary

3. Per-turn Metrics

4. Overall Metrics

Future Enhancements

1. Transcript Summarizer: Automatically converts the full conversation into a short summary using HuggingFace summarization pipeline (e.g., facebook/bart-large-cnn).
2. Dynamic Top-N Controls - User-selectable value to control "Top N" fillers and common words.
3. Downloadable Reports - Export full summary as PDF or CSV.
4. Stopword Filtering Toggle - Let user choose to ignore or include common stopwords. (including single characters)
5. UI Enhancements - Streamlit theming with .streamlit/config.toml.
6. Voice Transcription Pipeline - Auto-transcribe audio using Whisper Plug into this dashboard. (Advanced)