Upload 2 files

chatllama3.py

@@ -0,0 +1,40 @@
+import streamlit as st
+from transformers import pipeline
+from transformers import AutoConfig, pipeline
+from langchain_community.llms import Ollama
+import time
+
+st.title('*ChatBot clone*')
+
+llm = Ollama(model='llama3:latest')
+
+def response_generator(prompt):
+    response = llm.invoke(prompt, stop=['<|eot_id|>'])
+    for word in response.split():
+        yield word + " "
+        time.sleep(0.05)
+
+
+# init chat history
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+# display chat history
+for message in st.session_state.messages:
+    with st.chat_message(message['role']):
+        st.markdown(message['content'])
+
+# accept user input
+if prompt := st.chat_input("What is up?"):
+    # add user message to user history
+    st.session_state.messages.append({'role':'user','content':prompt})
+    # display user message
+    with st.chat_message('user'):
+        st.markdown(prompt)
+    
+    # display assistant response
+    with st.chat_message('assistant'):
+        ans = llm.invoke(prompt, stop=['<|eot_id|>'])
+        respose = st.write_stream(response_generator(prompt))
+    st.session_state.messages.append({'role':'assistant', 'content':respose})
+

requirements.txt

@@ -0,0 +1,13 @@
+langchain==0.0.184
+PyPDF2==3.0.1
+python-dotenv==1.0.0
+streamlit==1.18.1
+faiss-cpu==1.7.4
+altair==4
+tiktoken==0.4.0
+# uncomment to use huggingface llms
+huggingface-hub==0.14.1
+
+# uncomment to use instructor embeddings
+InstructorEmbedding==1.0.1
+sentence-transformers==2.2.2