Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+
+# Load the pre-trained model (you can replace this with any healthcare model from Hugging Face)
+model = pipeline("text-classification", model="mrm8488/bert-tiny-finetuned-sms-spam-detection")
+
+# Function to diagnose based on symptoms
+def diagnose(symptoms):
+    prediction = model(symptoms)
+    return f"Possible diagnosis: {prediction[0]['label']}"
+
+# Triage function based on symptoms
+def triage(symptoms):
+    if "chest pain" in symptoms or "shortness of breath" in symptoms:
+        return "Urgent: Seek immediate medical attention."
+    elif "fever" in symptoms and "cough" in symptoms:
+        return "Mild: Likely a viral infection, monitor symptoms."
+    else:
+        return "Non-urgent: No immediate concern."
+
+# Combine Diagnosis and Triage
+def full_check(symptoms):
+    diagnosis = diagnose(symptoms)
+    severity = triage(symptoms)
+    return diagnosis, severity
+
+# Create the Gradio interface
+iface = gr.Interface(fn=full_check, inputs="text", outputs=["text", "text"], title="Sehat Guard - Symptom Checker")
+
+# Launch the app
+iface.launch()