Update app.py

app.py

@@ -1,55 +1,40 @@
 from langchain_huggingface import HuggingFacePipeline, ChatHuggingFace
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
-import re
-import streamlit as st
 from profanity_check import predict
-
+import streamlit as st
 
 st.set_page_config(page_title="Chef Medi", layout="centered")
 st.title("Chef Medi")
 st.write("Share what you want to cook...")
 
-# Chat Template
-
 chat_template = ChatPromptTemplate.from_messages([
-    ('system', 
-     'Your name is Medi and you are an AI five star michelin star chef who teaches cooking. Your job is to guide users and help them create delicious food. Write minimal text to teach users answer in bullet points'),
+    ('system', 'You are Medi, a concise 5-star Michelin chef AI. Teach in bullet points.'),
     MessagesPlaceholder(variable_name='chat_history'),
     ('human', '{user_input}')
 ])
 
-
-# History Maintainence
-
 chat_history = []
 
-# Model
-
 llm = HuggingFacePipeline.from_model_id(
     model_id='TinyLlama/TinyLlama-1.1B-Chat-v1.0',
     task='text-generation',
     pipeline_kwargs=dict(
-        max_new_tokens = 512,
-        temperature = 0.1
+        max_new_tokens=128,
+        temperature=0.7
     )
 )
 
 model = ChatHuggingFace(llm=llm)
 
-
 def model_answer(user_input):
-
-    chat_history.append({'role': 'user', 'content': user_input})
+    recent_history = chat_history[-3:]
+    recent_history.append({'role': 'user', 'content': user_input})
     prompt = chat_template.invoke({
-        'chat_history': chat_history,
+        'chat_history': recent_history,
         'user_input': user_input
     })
     result = model.invoke(prompt)
-    
-    match = re.search(r"<\|assistant\|>(.*)", result.content, re.DOTALL)
-    ai_resp = match.group(1).strip()
-    return ai_resp
-
+    return result.content.strip()
 
 prompt = st.chat_input("Say something")
 
@@ -60,4 +45,6 @@ if prompt:
         ai_resp = "Please refrain from using bad language. Thanks"
     else:
         ai_resp = model_answer(prompt)
-    st.write(f"Medi: {ai_resp}")
+        chat_history.append({'role': 'user', 'content': prompt})
+        chat_history.append({'role': 'assistant', 'content': ai_resp})
+    st.write(f"Medi: {ai_resp}")