updated README.

README.md

@@ -1,19 +1,97 @@
----
-title: Emotion Classifier Deploy
-emoji: 🚀
-colorFrom: red
-colorTo: red
-sdk: docker
-app_port: 8501
-tags:
-- streamlit
-pinned: false
-short_description: Streamlit template space
----
-
-# Welcome to Streamlit!
-
-Edit `/src/streamlit_app.py` to customize this app to your heart's desire. :heart:
-
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+# Multi-Label Emotion Classifier
+
+A fine-tuned RoBERTa-based multi-label emotion classifier with a Streamlit web interface. The model predicts five emotions: **anger**, **fear**, **joy**, **sadness**, and **surprise**.
+
+[![Hugging Face](https://img.shields.io/badge/🤗%20Model-JashMevada%2Femotion--classifier-blue)](https://huggingface.co/JashMevada/emotion-classifier)
+[![Streamlit](https://img.shields.io/badge/Streamlit-App-FF4B4B?logo=streamlit)](https://huggingface.co/spaces/JashMevada/emotion-classifier-deploy)
+
+## Features
+
+- **Multi-label Classification**: Detects multiple emotions in a single text
+- **Fine-tuned RoBERTa-Large**: Built on `FacebookAI/roberta-large` for robust performance
+- **Interactive Web UI**: Streamlit-based interface with adjustable decision threshold
+- **Batch Inference**: Process multiple texts at once
+- **Visualization**: Bar charts showing emotion probabilities
+
+
+## Quick Start
+
+### Option 1: Run with Docker
+
+```bash
+docker build -t emotion-classifier .
+
+docker run -p 8501:8501 emotion-classifier
+```
+
+Open your browser at `http://localhost:8501`
+
+### Option 2: Run Locally
+
+```bash
+pip install -r requirements.txt
+
+streamlit run src/streamlit_app.py
+```
+
+## Model Architecture
+
+- **Base Model**: `FacebookAI/roberta-large`
+- **Classification Head**: 
+  - Linear(1024 → 1024) → ReLU → Dropout(0.2) → Linear(1024 → 5)
+- **Pooling**: Mean pooling with attention mask
+- **Output**: Sigmoid activation for multi-label classification
+
+## Usage
+
+### Web Interface
+
+1. Enter one or more text lines in the text area
+2. Adjust the decision threshold using the sidebar slider
+3. Click "Run inference" to get predictions
+4. View results in the table and bar chart
+
+### Python API
+
+```python
+from transformers import AutoModel, AutoTokenizer
+from huggingface_hub import hf_hub_download
+import torch
+
+# Load model components
+repo_id = "JashMevada/emotion-classifier"
+tokenizer = AutoTokenizer.from_pretrained(repo_id)
+encoder = AutoModel.from_pretrained(repo_id, add_pooling_layer=False)
+
+# Load classifier head
+weights_path = hf_hub_download(repo_id, "classifier.pth")
+classifier = torch.nn.Sequential(
+    torch.nn.Linear(1024, 1024),
+    torch.nn.ReLU(),
+    torch.nn.Dropout(0.2),
+    torch.nn.Linear(1024, 5),
+)
+classifier.load_state_dict(torch.load(weights_path, map_location="cpu"))
+
+# Inference
+text = "I am so happy today!"
+encoded = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
+
+with torch.no_grad():
+    outputs = encoder(**encoded).last_hidden_state
+    mask = encoded["attention_mask"].unsqueeze(-1).float()
+    pooled = (outputs * mask).sum(dim=1) / mask.sum(dim=1).clamp(min=1e-9)
+    logits = classifier(pooled)
+    probs = torch.sigmoid(logits)
+
+emotions = ["anger", "fear", "joy", "sadness", "surprise"]
+for emo, prob in zip(emotions, probs[0]):
+    print(f"{emo}: {prob:.3f}")
+```
+
+
+
+## Links
+
+- **Model**: [HuggingFace Hub](https://huggingface.co/JashMevada/emotion-classifier)
+- **Demo**: [Streamlit App](https://huggingface.co/spaces/JashMevada/emotion-classifier-deploy)

src/streamlit_app.py

@@ -8,7 +8,7 @@ import torch
 from huggingface_hub import hf_hub_download
 from transformers import AutoModel, AutoTokenizer
 
-st.set_page_config(page_title="Emotion Classifier", page_icon="😊", layout="wide")
+st.set_page_config(page_title="Emotion Classifier", layout="wide")
 
 EMOTIONS = ["anger", "fear", "joy", "sadness", "surprise"]
 BASE_MODEL_ID = "FacebookAI/roberta-large"
@@ -88,7 +88,7 @@ st.write(
 
 with st.sidebar:
     st.subheader("Inference Settings")
-    decision_threshold = st.slider("Decision threshold", 0.1, 0.9, 0.5, 0.01)
+    decision_threshold = st.slider("Decision threshold", 0.1, 0.9, 0.86, 0.01)
     st.caption("Threshold was tuned on validation data; tweak it to prioritize precision or recall.")
 
 sample_texts = {