Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+from transformers import pipeline
+
+# Load the dataset
+@st.cache_data
+def load_data():
+    return pd.read_csv("insurance_data.csv")
+
+data = load_data()
+
+# Load NLP model for intent detection
+@st.cache_resource
+def load_nlp_model():
+    return pipeline("text-classification", model="facebook/bart-large-mnli")
+
+classifier = load_nlp_model()
+
+# Streamlit UI
+st.title("Health Insurance Coverage Assistant")
+user_input = st.text_input("Enter your query (e.g., coverage for diabetes, best plans, etc.)")
+
+if user_input:
+    # Detect intent
+    labels = ["coverage explanation", "plan recommendation"]
+    result = classifier(user_input, candidate_labels=labels)
+    intent = result["labels"][0]  # Get the most likely intent
+
+    if intent == "coverage explanation":
+        st.subheader("Coverage Details")
+        condition_matches = data[data["Medical Condition"].str.contains(user_input, case=False, na=False)]
+        if not condition_matches.empty:
+            st.write(condition_matches)
+        else:
+            st.write("No specific coverage found for this condition.")
+    
+    elif intent == "plan recommendation":
+        st.subheader("Recommended Plans")
+        recommended_plans = data.sort_values(by=["Coverage (%)"], ascending=False).head(5)
+        st.write(recommended_plans)
+    
+    else:
+        st.write("Sorry, I couldn't understand your request. Please try again!")