app.py

import gradio as gr
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch
import numpy as np

# Load the model and tokenizer
model_name = "AventIQ-AI/distilbert-disease-specialist-recommendation"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)

# Define the labels
labels = ["Cardiology", "Neurology", "Orthopedics", "Dermatology"]

# Define the prediction function
def recommend_specialist(symptoms):
    inputs = tokenizer(symptoms, return_tensors="pt", padding=True, truncation=True, max_length=512).to(device)
    outputs = model(**inputs)
    probabilities = torch.nn.functional.softmax(outputs.logits, dim=1)[0]
    confidence, predicted_class = torch.max(probabilities, dim=0)
    recommended_specialist = labels[predicted_class]
    return f"Recommended Specialist: {recommended_specialist} (Confidence: {confidence.item():.2f})"

# Create the Gradio interface
iface = gr.Interface(
    fn=recommend_specialist,
    inputs=gr.Textbox(label="📝 Describe Your Symptoms", placeholder="e.g., experiencing chest pain and shortness of breath...", lines=3),
    outputs=gr.Textbox(label="🔍 Specialist Recommendation", interactive=True),
    title="🏥 Medical Specialist Recommender",
    description="Enter your symptoms to receive a recommendation for the appropriate medical specialist.",
    theme="compact",
    allow_flagging="never",
    examples=[
        ["I have persistent headaches and dizziness."],
        ["My skin has developed a red, itchy rash."],
        ["I'm experiencing joint pain and stiffness in the mornings."]
    ],
)

if __name__ == "__main__":
    iface.launch()