Update README.md

README.md

@@ -1,3 +1,81 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+language: en
+datasets:
+- gretelai/symptom_to_diagnosis
+metrics:
+- f1
+pipeline_tag: text-classification
+widget:
+- text: "I have a sharp pain in my chest, difficulty breathing, and a persistent cough."
+---
+
+# Symptom-to-Condition Classifier
+
+This repository contains the artefacts for a LightGBM classification model that predicts a likely medical condition based on a user's textual description of their symptoms.
+
+**This model is a proof-of-concept for a portfolio project and is NOT a medical diagnostic tool.**
+
+## Model Details
+
+This is not a standard end-to-end transformer model. It is a classical machine learning pipeline that uses pre-trained transformers for feature extraction.
+
+- **Feature Extractor:** The model uses embeddings from `emilyalsentzer/Bio_ClinicalBERT`. Specifically, it generates a 768-dimension vector for each symptom description by applying **mean pooling** to the last hidden state of the BERT model.
+- **Classifier:** The actual classification is performed by a `LightGBM` (Light Gradient Boosting Machine) model trained on the embeddings.
+
+## Intended Use
+
+This model is intended for educational and demonstrational purposes only. It takes a string of text describing symptoms and outputs a predicted medical condition from a predefined list of 22 classes.
+
+### Ethical Considerations & Limitations
+
+- **⚠️ Not for Medical Use:** This model should **NEVER** be used to diagnose, treat, or provide medical advice for real-world health issues. It is not a substitute for consultation with a qualified healthcare professional.
+- **Data Bias:** The model's knowledge is limited to the `gretelai/symptom_to_diagnosis` dataset. It cannot predict any condition outside of its 22-class training data and may perform poorly on symptom descriptions that are stylistically different from the training set.
+- **Correlation, Not Causation:** The model learns statistical correlations between words and labels. It has no true understanding of biology or medicine.
+
+## How to Use
+
+To use this model, you must load the feature extractor (`Bio_ClinicalBERT`), the LightGBM classifier, and the label encoder.
+
+```python
+import torch
+import joblib
+from transformers import AutoTokenizer, AutoModel
+from huggingface_hub import hf_hub_download
+
+# --- CONFIGURATION ---
+HF_REPO_ID = "<YourUsername>/Symptom-to-Condition-Classifier" # Replace with your repo ID
+LGBM_MODEL_FILENAME = "lgbm_disease_classifier.joblib"
+LABEL_ENCODER_FILENAME = "label_encoder.joblib"
+BERT_MODEL_NAME = "emilyalsentzer/Bio_ClinicalBERT"
+
+# --- LOAD ARTIFACTS ---
+tokenizer = AutoTokenizer.from_pretrained(BERT_MODEL_NAME)
+bert_model = AutoModel.from_pretrained(BERT_MODEL_NAME)
+lgbm_model_path = hf_hub_download(repo_id=HF_REPO_ID, filename=LGBM_MODEL_FILENAME)
+label_encoder_path = hf_hub_download(repo_id=HF_REPO_ID, filename=LABEL_ENCODER_FILENAME)
+lgbm_model = joblib.load(lgbm_model_path)
+label_encoder = joblib.load(label_encoder_path)
+
+# --- INFERENCE PIPELINE ---
+def mean_pool(model_output, attention_mask):
+    token_embeddings = model_output.last_hidden_state
+    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+    sum_embeddings = torch.sum(token_embeddings * input_mask_expanded, 1)
+    sum_mask = torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+    return sum_embeddings / sum_mask
+
+def predict_condition(text):
+    encoded_input = tokenizer(text, padding=True, truncation=True, max_length=256, return_tensors='pt')
+    with torch.no_grad():
+        model_output = bert_model(**encoded_input)
+    embedding = mean_pool(model_output, encoded_input['attention_mask'])
+    
+    prediction_id = lgbm_model.predict(embedding.cpu().numpy())
+    predicted_condition = label_encoder.inverse_transform(prediction_id)[0]
+    return predicted_condition
+
+# --- EXAMPLE ---
+symptoms = "I have a burning sensation in my stomach that gets worse when I haven't eaten."
+prediction = predict_condition(symptoms)
+print(f"Predicted Condition: {prediction}")