Update README.md

README.md

@@ -12,3 +12,91 @@ short_description: Compare sentiment predictions from two deep learning models
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+# 📊 Sentiment Model Comparison App
+
+This Streamlit app compares two sentiment classification models trained on IMDB movie reviews.
+
+- Model A: 6M params, 50k vocab (fast & lightweight)
+- Model B: 34M params, 256k vocab (high capacity)
+- Ensemble: Average of both predictions
+
+🔗 **Live Demo:** [Try it on Spaces](https://huggingface.co/spaces/Daksh0505/sentiment-model-comparison)
+
+---
+
+## 🔍 Features
+
+- Enter single review text or upload a CSV (`review` column)
+- Get predictions from both models + ensemble average
+- Compare probabilities visually
+- Submit feedback (saved to Google Sheets)
+
+---
+
+## 📁 Dataset
+
+- **Source:** [IMDB Multi-Movie Dataset](https://huggingface.co/datasets/Daksh0505/IMDB-Reviews)
+
+```bibtex
+@misc{imdb-multimovie-reviews,
+  title = {IMDb Multi-Movie Review Dataset},
+  author = {Daksh Bhardwaj},
+  year = {2025},
+  url = {https://huggingface.co/datasets/Daksh0505/IMDB-Reviews}
+}
+
+---
+
+## 🧠 Models
+
+### 🔹 Model A
+- Filename: `sentiment_model_imdb_6.6M.keras`  
+- **Trainable Parameters**: ~6.6 million  
+- **Total Parameters**: ~13.06 million  
+- **Vocabulary Size**: 50,000 tokens  
+- Description: Lightweight and efficient; optimized for speed.
+
+### 🔹 Model B
+- Filename: `sentiment_model_imdb_34M.keras`  
+- **Trainable Parameters**: ~34 million  
+- **Total Parameters**: ~99.43 million  
+- **Vocabulary Size**: 256,000 tokens  
+- Description: Larger and more expressive; higher accuracy on nuanced reviews.
+
+---
+
+## 🗂 Tokenizers
+
+Each model uses its own tokenizer in Keras JSON format:
+
+- `tokenizer_50k.json` → used with Model A  
+- `tokenizer_256k.json` → used with Model B  
+
+---
+
+## 🔧 Load Models & Tokenizers (from Hugging Face Hub)
+
+```python
+from huggingface_hub import hf_hub_download
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing.text import tokenizer_from_json
+import json
+
+# === Model A ===
+model_path_a = hf_hub_download(repo_id="Daksh0505/sentiment-model-imdb", filename="sentiment_model_imdb_6.6M.keras")
+tokenizer_path_a = hf_hub_download(repo_id="Daksh0505/sentiment-model-imdb", filename="tokenizer_50k.json")
+
+with open(tokenizer_path_a, "r") as f:
+    tokenizer_a = tokenizer_from_json(json.load(f))
+
+model_a = load_model(model_path_a)
+
+# === Model B ===
+model_path_b = hf_hub_download(repo_id="Daksh0505/sentiment-model-imdb", filename="sentiment_model_imdb_34M.keras")
+tokenizer_path_b = hf_hub_download(repo_id="Daksh0505/sentiment-model-imdb", filename="tokenizer_256k.json")
+
+with open(tokenizer_path_b, "r") as f:
+    tokenizer_b = tokenizer_from_json(json.load(f))
+
+model_b = load_model(model_path_b)