Update app.py

app.py

@@ -9,10 +9,10 @@ def load_data():
 
 data = load_data()
 
-# Load NLP model for intent detection using DeepSeek
+# Load DeepSeek model (General text classification)
 @st.cache_resource
 def load_nlp_model():
-    return pipeline("zero-shot-classification", model="deepseek-ai/deepseek-llm-7b-chat")
+    return pipeline("text-classification", model="deepseek-ai/deepseek-llm-7b-chat")
 
 classifier = load_nlp_model()
 
@@ -21,16 +21,17 @@ 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)
-    
-    # Check if DeepSeek returns results properly
-    if "labels" in result:
-        intent = result["labels"][0]  # Get the most likely intent
+    # Detect intent using text classification
+    result = classifier(user_input)  
+    label = result[0]["label"]  # Get predicted label
+
+    # Manual mapping (since DeepSeek does not support `candidate_labels`)
+    if "coverage" in user_input.lower():
+        intent = "coverage explanation"
+    elif "recommend" in user_input.lower() or "best plan" in user_input.lower():
+        intent = "plan recommendation"
     else:
-        st.write("Intent detection failed. Please try again.")
-        intent = None
+        intent = "unknown"
 
     if intent == "coverage explanation":
         st.subheader("Coverage Details")