Create app.py

app.py

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+from langchain_huggingface import HuggingFacePipeline, ChatHuggingFace
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+import re
+import streamlit as st
+from profanity_check import predict
+
+
+
+# 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'),
+    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
+    )
+)
+
+model = ChatHuggingFace(llm=llm)
+
+
+def model_answer(user_input):
+
+    chat_history.append({'role': 'user', 'content': user_input})
+    prompt = chat_template.invoke({
+        'chat_history': chat_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
+
+
+prompt = st.chat_input("Say something")
+
+if prompt:
+    st.write(f"You: {prompt}")
+    offensive = predict([prompt])
+    if offensive == [1]:
+        ai_resp = "Please refrain from use bad language. Thanks"
+    else:
+        ai_resp = model_answer(prompt)
+    st.write(f"Medi: {ai_resp}")