Upload virtualhealth.py

virtualhealth.py

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+import xgboost as xgb
+import pickle
+import numpy as np
+import pandas as pd
+import torch
+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForQuestionAnswering
+import nltk
+from nltk.tokenize import word_tokenize
+from nltk.corpus import stopwords
+import re
+
+# 🔹 Download stopwords only when needed
+nltk.download("stopwords")
+nltk.download("punkt")
+nltk.download('punkt_tab')
+
+# Load English stopwords
+stop_words = set(stopwords.words("english"))
+
+# ============================
+# 🔹 1. Load Pretrained Medical Q&A Model
+# ============================
+# qa_model_name = "deepset/roberta-base-squad2"  # Better model for medical Q&A
+# tokenizer = AutoTokenizer.from_pretrained(qa_model_name)
+# qa_model = AutoModelForQuestionAnswering.from_pretrained(qa_model_name)
+model_name = "dmis-lab/biobert-large-cased-v1.1-squad"  # ✅ Updated Model
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+qa_model = AutoModelForQuestionAnswering.from_pretrained(model_name)
+# ============================
+# 🔹 2. Load Symptom Checker Model
+# ============================
+model = xgb.XGBClassifier()
+model.load_model("symptom_disease_model.json")  # Load trained model
+label_encoder = pickle.load(open("label_encoder.pkl", "rb"))  # Load label encoder
+X_train = pd.read_csv("X_train.csv")  # Load symptoms
+symptom_list = X_train.columns.tolist()
+
+# ============================
+# 🔹 3. Load Precaution Data
+# ============================
+precaution_df = pd.read_csv("Disease precaution.csv")
+precaution_dict = {
+    row["Disease"].strip().lower(): [row[f"Precaution_{i}"] for i in range(1, 5) if pd.notna(row[f"Precaution_{i}"])]
+    for _, row in precaution_df.iterrows()
+}
+
+# ============================
+# 🔹 4. Load Medical Context
+# ============================
+def load_medical_context():
+    with open("medical_context.txt", "r", encoding="utf-8") as file:
+        return file.read()
+
+medical_context = load_medical_context()
+
+# ============================
+# 🔹 5. Doctor Database
+# ============================
+doctor_database = {
+    "malaria": [{"name": "Dr. Rajesh Kumar", "specialty": "Infectious Diseases", "location": "Apollo Hospital", "contact": "9876543210"}],
+    "diabetes": [{"name": "Dr. Anil Mehta", "specialty": "Endocrinologist", "location": "AIIMS Delhi", "contact": "9876543233"}],
+    "heart attack": [{"name": "Dr. Vikram Singh", "specialty": "Cardiologist", "location": "Medanta Hospital", "contact": "9876543255"}],
+}
+
+# ============================
+# 🔹 6. Predict Disease from Symptoms
+# ============================
+def predict_disease(user_symptoms):
+    """Predicts disease based on user symptoms using the trained XGBoost model."""
+    input_vector = np.zeros(len(symptom_list))
+
+    for symptom in user_symptoms:
+        if symptom in symptom_list:
+            input_vector[symptom_list.index(symptom)] = 1
+
+    input_vector = input_vector.reshape(1, -1)  # Reshape for model input
+    predicted_class = model.predict(input_vector)[0]  # Predict disease
+    predicted_disease = label_encoder.inverse_transform([predicted_class])[0]
+
+    return predicted_disease
+
+# ============================
+# 🔹 7. Get Precautions for a Disease
+# ============================
+def get_precautions(disease):
+    """Returns the precautions for a given disease."""
+    return precaution_dict.get(disease.lower(), ["No precautions available"])
+
+# ============================
+# 🔹 8. Answer Medical Questions (Q&A Model)
+# ============================
+def get_medical_answer(question):
+    """Uses the pre-trained Q&A model to answer general medical questions."""
+    inputs = tokenizer(question, medical_context, return_tensors="pt", truncation=True, max_length=512)
+    with torch.no_grad():
+        outputs = qa_model(**inputs)
+
+    answer_start = torch.argmax(outputs.start_logits)
+    answer_end = torch.argmax(outputs.end_logits) + 1
+
+    answer = tokenizer.convert_tokens_to_string(
+        tokenizer.convert_ids_to_tokens(inputs["input_ids"][0][answer_start:answer_end])
+    )
+
+    if answer.strip() in ["", "[CLS]", "<s>"]:
+        return "I'm not sure. Please consult a medical professional."
+    
+    return answer
+# ============================
+# 🔹 9. Book a Doctor's Appointment
+# ============================
+def book_appointment(disease):
+    """Finds a doctor for the given disease and returns appointment details."""
+    disease = disease.lower().strip()
+    doctors = doctor_database.get(disease, [])
+    if not doctors:
+        return f"Sorry, no available doctors found for {disease}."
+
+    doctor = doctors[0]
+    return f"Appointment booked with **{doctor['name']}** ({doctor['specialty']}) at **{doctor['location']}**.\nContact: {doctor['contact']}"
+
+# ============================
+# 🔹 10. Handle User Queries
+# ============================
+def handle_user_query(user_query):
+    """Handles user queries related to symptoms, diseases, and doctor appointments."""
+    user_query = user_query.lower().strip()
+
+    # Check if query is about symptoms
+    if "symptoms" in user_query or "signs" in user_query:
+        disease = user_query.replace("symptoms", "").replace("signs", "").strip()
+        return get_medical_answer(f"What are the symptoms of {disease}?")
+
+    # Check if query is about treatment
+    elif "treatment" in user_query or "treat" in user_query:
+        disease = user_query.replace("treatment", "").replace("treat", "").strip()
+        return get_medical_answer(f"What is the treatment for {disease}?")
+
+    # Check for doctor recommendation
+    elif "who should i see" in user_query:
+        disease = user_query.replace("who should i see for", "").strip()
+        return book_appointment(disease)
+
+    # Check for appointment booking
+    elif "book appointment" in user_query:
+        disease = user_query.replace("book appointment for", "").strip()
+        return book_appointment(disease)
+
+    # Default case: general medical question
+    else:
+        return get_medical_answer(user_query)