Update app.py

app.py

@@ -1,20 +1,11 @@
-#!/usr/bin/env python
-# coding: utf-8
-
-# In[ ]:
-
-
 import streamlit as st
 import nltk
-from nltk.stem import WordNetLemmatizer
 import pickle
 import numpy as np
 from tensorflow.keras.models import load_model
+from nltk.stem import WordNetLemmatizer
 
-nltk.download('punkt')
-nltk.download('wordnet')
-
-# Load saved model and other necessary files
+# Load the pre-trained model and other data
 model = load_model("chatbot_model.h5")
 words = pickle.load(open('words.pkl', 'rb'))
 classes = pickle.load(open('classes.pkl', 'rb'))
@@ -40,28 +31,24 @@ def bow(sentence, words, show_details=True):
 
 # Streamlit app
 def main():
-    st.title("Chatbot App")
-    st.write("Welcome to the chatbot! Start a conversation.")
+    st.title("Healthcare Chatbot")
+    st.write("Welcome to the Healthcare Chatbot! Enter your symptoms below.")
 
-    user_input = st.text_input("You: ")
-    if st.button("Send"):
+    user_input = st.text_input("You:")
+    if st.button("Predict"):
         if user_input.strip() == "":
-            st.write("Bot: Please enter a message.")
+            st.write("Bot: Please enter your symptoms.")
         else:
             p = bow(user_input, words)
             res = model.predict(np.array([p]))[0]
             ERROR_THRESHOLD = 0.25
             results = [[i, r] for i, r in enumerate(res) if r > ERROR_THRESHOLD]
             results.sort(key=lambda x: x[1], reverse=True)
-            return_list = []
             for r in results:
-                return_list.append({"intent": classes[r[0]], "probability": str(r[1])})
-            for i in intents["intents"]:
-                if i["tag"] == return_list[0]["intent"]:
-                    response = np.random.choice(i["responses"])
-                    break
-            st.write("Bot:", response)
+                return_class = classes[r[0]]
+                break
+
+            st.write("Bot: Based on your symptoms, you might have:", return_class)
 
 if __name__ == "__main__":
     main()
-