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README.md
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This application allows you to analyze customer feedback using LLMs (Large Language Models) with custom prompts. You can upload Excel files containing customer feedback, specify analysis instructions, and process the feedback in batches.
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- Pydantic
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- OpenPyXL
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## Installation
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```bash
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pip install streamlit pandas openpyxl pydantic openai python-dotenv
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```
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## Usage
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1.
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```bash
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streamlit run app.py
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```
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2. Open the app in your browser (typically at http://localhost:8501)
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3. Enter your OpenAI API key in the sidebar
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4. Upload an Excel file containing customer feedback
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5. Select the column containing the feedback text
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- `models.py`: Pydantic models for data validation
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- `utils.py`: Utility functions for processing and logging
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- `data/`: Directory for sample and processed data
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- `logs/`: Directory for application logs
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## Example Prompt Templates
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---
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title: LLM Feedback Analyzer
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emoji: 📊
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colorFrom: blue
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colorTo: indigo
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sdk: streamlit
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sdk_version: 1.44.1
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app_file: app.py
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pinned: false
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---
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# LLM Feedback Analyzer
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This application allows you to analyze customer feedback using LLMs (Large Language Models) with custom prompts. You can upload Excel files containing customer feedback, specify analysis instructions, and process the feedback in batches.
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- Pydantic
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- OpenPyXL
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## Usage
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1. Enter your OpenAI API key in the sidebar
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2. Upload an Excel file containing customer feedback
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3. Select the column containing the feedback text
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4. Customize the analysis prompt as needed
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5. Click "Process Feedback" to start the analysis
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6. Download the results when processing is complete
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## Example Prompt Templates
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