Upload app.py

app.py

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+import streamlit as st
+import random
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+import nltk
+from nltk.corpus import stopwords
+nltk.download('stopwords')
+
+# Sample dataset with categories, questions, and answers
+data = {
+    "Musculoskeletal Disorders": [
+        ("What exercises can be recommended for a patient with lower back pain?", 
+         "Exercises like McKenzie extensions, core strengthening, and pelvic tilts can help alleviate pain.")
+    ],
+    "Neurological Disorders": [
+        ("How do you assess balance in a patient post-stroke?", 
+         "You can use tests like the Berg Balance Scale or the Functional Reach Test.")
+    ],
+    "Sports Injuries": [
+        ("How do you prevent recurrent ankle sprains in athletes?", 
+         "Proprioception training, strengthening peroneal muscles, and using ankle supports can prevent recurrent sprains.")
+    ],
+}
+
+# Preprocess user input and answers by removing stopwords
+def preprocess_text(text):
+    stop_words = set(stopwords.words('english'))
+    return ' '.join([word for word in text.split() if word.lower() not in stop_words])
+
+# Function to calculate similarity score
+def calculate_similarity(user_answer, correct_answer):
+    vectorizer = TfidfVectorizer().fit_transform([preprocess_text(user_answer), preprocess_text(correct_answer)])
+    vectors = vectorizer.toarray()
+    score = cosine_similarity([vectors[0]], [vectors[1]])[0][0]
+    return round(score * 100, 2)  # Return as percentage
+
+# Streamlit app starts here
+st.title("Physiotherapy Virtual Patient Interaction")
+
+# Step 1: User chooses a category
+category = st.selectbox("Choose a Category", list(data.keys()))
+
+if category:
+    # Step 2: Random question from the chosen category
+    question, correct_answer = random.choice(data[category])
+    st.write(f"**Patient's Question:** {question}")
+    
+    # Step 3: User input their answer
+    user_answer = st.text_area("Your Response", "")
+    
+    # Step 4: Show similarity score
+    if st.button("Submit Answer"):
+        if user_answer.strip():
+            similarity_score = calculate_similarity(user_answer, correct_answer)
+            st.write(f"**Similarity Score:** {similarity_score}%")
+        else:
+            st.write("Please provide an answer before submitting.")
+