Update app.py

app.py

@@ -27,44 +27,65 @@
 # chat()
 
 
+# import streamlit as st
+# from transformers import BlenderbotTokenizer, BlenderbotForConditionalGeneration
+
+# # Load BlenderBot model and tokenizer
+# model_name = "facebook/blenderbot-400M-distill"
+# tokenizer = BlenderbotTokenizer.from_pretrained(model_name)
+# model = BlenderbotForConditionalGeneration.from_pretrained(model_name)
+
+# # Function to generate a response from BlenderBot
+# def get_blenderbot_response(input_text):
+#     inputs = tokenizer(input_text, return_tensors="pt")
+#     reply_ids = model.generate(**inputs)
+#     response = tokenizer.decode(reply_ids[0], skip_special_tokens=True)
+#     return response
+
+# # Streamlit app
+# st.title("Insurance Q&A Chatbot")
+# st.write("Ask any question about insurance, and I'll do my best to help!")
+
+# # Chat history
+# if "history" not in st.session_state:
+#     st.session_state.history = []
+
+# # Input text box for user
+# user_input = st.text_input("You:", "")
+
+# # Respond to user input
+# if user_input:
+#     # Add user question to history
+#     st.session_state.history.append({"user": user_input})
+
+#     # Generate bot response
+#     response = get_blenderbot_response(user_input)
+#     st.session_state.history.append({"bot": response})
+
+# # Display chat history
+# for message in st.session_state.history:
+#     if "user" in message:
+#         st.write("**You:**", message["user"])
+#     if "bot" in message:
+#         st.write("**Bot:**", message["bot"])
+
 import streamlit as st
-from transformers import BlenderbotTokenizer, BlenderbotForConditionalGeneration
-
-# Load BlenderBot model and tokenizer
-model_name = "facebook/blenderbot-400M-distill"
-tokenizer = BlenderbotTokenizer.from_pretrained(model_name)
-model = BlenderbotForConditionalGeneration.from_pretrained(model_name)
-
-# Function to generate a response from BlenderBot
-def get_blenderbot_response(input_text):
-    inputs = tokenizer(input_text, return_tensors="pt")
-    reply_ids = model.generate(**inputs)
-    response = tokenizer.decode(reply_ids[0], skip_special_tokens=True)
-    return response
-
-# Streamlit app
-st.title("Insurance Q&A Chatbot")
-st.write("Ask any question about insurance, and I'll do my best to help!")
-
-# Chat history
-if "history" not in st.session_state:
-    st.session_state.history = []
-
-# Input text box for user
-user_input = st.text_input("You:", "")
-
-# Respond to user input
-if user_input:
-    # Add user question to history
-    st.session_state.history.append({"user": user_input})
-
-    # Generate bot response
-    response = get_blenderbot_response(user_input)
-    st.session_state.history.append({"bot": response})
-
-# Display chat history
-for message in st.session_state.history:
-    if "user" in message:
-        st.write("**You:**", message["user"])
-    if "bot" in message:
-        st.write("**Bot:**", message["bot"])
+from rasa.core.agent import Agent
+from rasa.shared.core.domain import Domain
+from rasa.shared.core.tracker_store import InMemoryTrackerStore
+from rasa.shared.nlu.interpreter import RasaNLUInterpreter
+
+# Load Rasa model
+domain = Domain.load("insurance_domain.yml")
+interpreter = RasaNLUInterpreter("insurance_nlu.pkl")
+tracker_store = InMemoryTrackerStore(domain)
+agent = Agent.load("insurance_model", interpreter=interpreter, tracker_store=tracker_store)
+
+# Define Streamlit app
+st.title("Insurance Chatbot")
+
+user_input = st.text_area("You:", height=200)
+
+if st.button("Send"):
+    response = agent.handle_text(user_input)
+    st.text_area("Bot:", value=response[0].text, height=200, max_chars=None, key=None)