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README.md

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-title: Caption Gen
-emoji: 📸
-sdk: streamlit
-sdk_version: 1.43.0
-app_file: app.py
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-\# AI Image Caption Generator
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-A deep learning–based image captioning system built using a \*\*ResNet50 encoder\*\* and an \*\*LSTM decoder\*\*. The model generates natural language descriptions for uploaded images.
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-\## Architecture
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-\* \*\*Encoder:\*\* ResNet50 (frozen backbone)
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-\* \*\*Decoder:\*\* LSTM-based sequence generator
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-\* \*\*Training Dataset:\*\* Flickr8k
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-\* \*\*Inference Framework:\*\* Streamlit
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-\* \*\*Evaluation Metric:\*\* SacreBLEU
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-The encoder extracts high-level visual features, which are then passed to the decoder to generate captions word by word.
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-\## How It Works
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-1\. User uploads an image.
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-2\. Image is preprocessed and passed through the ResNet50 encoder.
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-3\. Extracted feature vector is fed into the LSTM decoder.
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-4\. Caption is generated using temperature-based sampling.
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-5\. If the image belongs to the Flickr8k dataset, BLEU metrics are displayed.
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-\## Features
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-\* Temperature-controlled caption generation
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-\* SacreBLEU evaluation
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-\* N-gram precision breakdown (1–4 gram)
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-\* Clean Streamlit interface
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-\* Fully CPU-compatible deployment
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-\## Project Structure
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-```
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-app.py
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-models/
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-    encoder.pth
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-    decoder.pth
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-models/
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-    encoder.py
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-    decoder.py
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-utils/
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-    transforms.py
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-    vocab.py
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-    helpers.py
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-vocabulary.json
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-requirements.txt
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-```
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-\## Model Details
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-\* Encoder weights size: ~92 MB
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-\* Decoder weights size: ~32 MB
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-\* Full encoder backbone included in state\_dict
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-\* Inference runs on CPU
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-\## Limitations
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-\* Trained on Flickr8k (8,000 images)
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-\* Performs best on outdoor scenes, people, and animals
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-\* May generalize poorly to unseen domains
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-\* CPU inference can be slow (2–5 seconds per image)
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-\## Setup (Local)
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-```bash
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-pip install -r requirements.txt
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-streamlit run app.py
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-```
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-\## Deployment
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-This project is deployed on \*\*Hugging Face Spaces\*\* using Streamlit.
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-\## License
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-MIT License
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-If you want, I can also write a \*\*short portfolio-style README\*\* optimized specifically for recruiters.
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