Delete virtualhealth.py

virtualhealth.py

@@ -1,152 +0,0 @@
-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)