📞 PostCallTranscriptionAnalyser

Analyze customer service call transcripts with AI!
Get actionable feedback, agent scoring, and complaint type detection — all powered by LLMs via Ollama.

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🚀 Features

• 🤖 Agent Scoring: Rates empathy, problem-solving, and professionalism.
• 📝 Complaint Type Detection: Identifies the main issue from a call.
• 🗣️ Speaker Feedback: Generates feedback for each unique speaker.

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🛠️ API Endpoints

✅ Health Check

GET /health
Returns API health status.

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🏅 Score Transcript and Extract Complaint

POST /score

Request:

{
  "transcript_id": "1"
}

Response:
Score, feedback, and complaint type in JSON.

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📋 Generate Call Report

POST /GenerateReport

Request:

{
  "transcript_id": "1"
}

Response:
Speaker-wise feedback in JSON.

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⚡ Quickstart

1. Install dependencies

   pip install -r requirements.txt

2. Start Ollama (ensure llama3 model is available)

3. Run the app

   python app.py

   Or with Docker:

   docker build -t postcall-analyser .
   docker run -p 5000:5000 postcall-analyser

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📂 Project Structure

• app.py — Flask API server
• TranscriptAnalyzer.py — Transcript analysis logic
• requirements.txt — Python dependencies
• Dockerfile — Containerization

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💡 Example Usage

curl -X POST http://localhost:5000/score -H "Content-Type: application/json" -d '{"transcript_id": "1"}'

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🧑‍💻 Requirements

• Python 3.11+
• Flask
• Ollama Python client
• Ollama server with llama3 model

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📄 License

The project is licensed under the MIT License. See the LICENSE file for more details.

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Made with ❤️ for call center QA prototyping.