prescription-classifier

💊 Prescription Classifier — Sanjeevani

A fine-tuned BERT-based text classification model built for the Sanjeevani healthcare platform. It classifies whether a given text input is a valid medical prescription or not, enabling automated prescription validation in the Sanjeevani ordering and delivery pipeline.

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🏥 Project Context

Sanjeevani is a full-stack healthcare platform that connects patients with pharmacies and delivery agents. It includes:

• 📱 Mobile App (Flutter) — for customers to browse medicines and place orders
• 🖥️ Admin Console (React) — for pharmacy/admin management
• 🚚 Delivery App (Flutter) — for delivery agents
• 🤖 AI Chatbots (WhatsApp & Telegram) — for conversational ordering
• 📞 Calling Assistant (VAPI) — voice-based ordering
• 🧠 This Model — prescription validation at the AI layer

This model is used by the backend services to verify uploaded prescriptions before allowing controlled medicine orders.

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🧠 Model Details

Labels

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🚀 Quick Start

from transformers import pipeline

classifier = pipeline(
    "text-classification",
    model="Samay-Verse/prescription-classifier"
)

result = classifier("Tab. Amoxicillin 500mg - 1 tablet twice daily for 5 days. Dr. Sharma")
print(result)
# [{'label': 'VALID_PRESCRIPTION', 'score': 0.97}]

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🔧 Manual Usage

from transformers import BertTokenizer, BertForSequenceClassification
import torch

model_name = "Samay-Verse/prescription-classifier"
tokenizer = BertTokenizer.from_pretrained(model_name)
model = BertForSequenceClassification.from_pretrained(model_name)

text = "Tab. Paracetamol 650mg - 1 tablet SOS. Sig: after food."
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)

with torch.no_grad():
    outputs = model(**inputs)
    prediction = torch.argmax(outputs.logits, dim=1).item()

print("Valid Prescription" if prediction == 1 else "Invalid")

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🏗️ Integration in Sanjeevani Backend

This model is called by the Sanjeevani backend API when a customer uploads a prescription image (OCR-extracted text) or types a prescription manually via the chatbot.

Customer uploads prescription
        ↓
OCR / Text Extraction
        ↓
prescription-classifier (this model)
        ↓
VALID → Order proceeds
INVALID → User prompted to re-upload

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📦 Training Details

• Base Model: bert-base-uncased
• Training Data: Curated dataset of Indian pharmaceutical prescriptions and non-prescription texts
• Preprocessing: Lowercased, tokenized with BertTokenizer, max length 512
• Framework: HuggingFace Transformers + PyTorch

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⚠️ Limitations & Disclaimer

• This model is designed for the Indian pharmaceutical context and may not generalize well to other regions.
• It is a supporting tool, not a replacement for professional medical verification.
• Always ensure compliance with local regulations when using AI for prescription validation.
• Model performance may vary on handwritten prescription OCR output.

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📄 License

Apache 2.0 — Free to use with attribution.

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🔗 Related Resources

• 🌐 Sanjeevani Platform: Samay-Verse on HuggingFace
• 🤗 Base Model: bert-base-uncased

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✉️ Contact

Built and maintained by the Samay-Verse team. For issues or contributions, open a discussion on the HuggingFace model page.