Use GPT2 Model

.streamlit/secrets.toml

@@ -0,0 +1,6 @@
+[theme]
+primaryColor="#F63366"
+backgroundColor="#FFFFFF"
+secondaryBackgroundColor="#F0F2F6"
+textColor="#262730"
+font="sans serif"

app.py

@@ -0,0 +1,58 @@
+import streamlit as st
+# from transformers import T5Tokenizer,AutoModelForCausalLM
+
+model_name = "rinna/japanese-gpt2-small"
+
+# tokenizer = T5Tokenizer.from_pretrained(model_name)
+# model = AutoModelForCausalLM.from_pretrained(model_name)
+
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Load the pre-trained GPT-2 model and tokenizer
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# App title
+st.set_page_config(page_title="ChatBot")
+
+if "messages" not in st.session_state.keys():
+    st.session_state.messages = [{"role": "assistant", "content": "How may I help you?"}]
+
+# Display chat messages
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.write(message["content"])
+
+# Function for generating LLM response
+# def generate_response(prompt_input):
+#     input = tokenizer.encode(prompt_input, return_tensors="pt")
+#     output = model.generate(input, do_sample=True, max_length=30, num_return_sequences=1)
+
+#     return tokenizer.batch_decode(output)
+
+def generate_response(prompt, max_length=50):
+    input_ids = tokenizer.encode(prompt, return_tensors="pt")
+
+    # Generate response
+    with torch.no_grad():
+        output = model.generate(input_ids, max_length=max_length, num_return_sequences=1, pad_token_id=50256)
+
+    response = tokenizer.decode(output[0], skip_special_tokens=True)
+    return response
+
+# User-provided prompt
+if prompt := st.chat_input():
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    with st.chat_message("user"):
+        st.write(prompt)
+
+# Generate a new response if last message is not from assistant
+if st.session_state.messages[-1]["role"] != "assistant":
+    with st.chat_message("assistant"):
+        with st.spinner("Thinking..."):
+            response = generate_response(prompt)
+            st.write(response) 
+    message = {"role": "assistant", "content": response}
+    st.session_state.messages.append(message)

app_hugchat.py

@@ -0,0 +1,55 @@
+import streamlit as st
+from hugchat import hugchat
+from hugchat.login import Login
+
+# App title
+st.set_page_config(page_title="🤗💬 HugChat")
+
+# Hugging Face Credentials
+with st.sidebar:
+    st.title('🤗💬 HugChat')
+    if ('EMAIL' in st.secrets) and ('PASS' in st.secrets):
+        st.success('HuggingFace Login credentials already provided!', icon='✅')
+        hf_email = st.secrets['EMAIL']
+        hf_pass = st.secrets['PASS']
+    else:
+        hf_email = st.text_input('Enter E-mail:', type='password')
+        hf_pass = st.text_input('Enter password:', type='password')
+        if not (hf_email and hf_pass):
+            st.warning('Please enter your credentials!', icon='⚠️')
+        else:
+            st.success('Proceed to entering your prompt message!', icon='👉')
+    st.markdown('📖 Learn how to build this app in this [blog](https://blog.streamlit.io/how-to-build-an-llm-powered-chatbot-with-streamlit/)!')
+    
+# Store LLM generated responses
+if "messages" not in st.session_state.keys():
+    st.session_state.messages = [{"role": "assistant", "content": "How may I help you?"}]
+
+# Display chat messages
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.write(message["content"])
+
+# Function for generating LLM response
+def generate_response(prompt_input, email, passwd):
+    # Hugging Face Login
+    sign = Login(email, passwd)
+    cookies = sign.login()
+    # Create ChatBot                        
+    chatbot = hugchat.ChatBot(cookies=cookies.get_dict())
+    return chatbot.chat(prompt_input)
+
+# User-provided prompt
+if prompt := st.chat_input(disabled=not (hf_email and hf_pass)):
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    with st.chat_message("user"):
+        st.write(prompt)
+
+# Generate a new response if last message is not from assistant
+if st.session_state.messages[-1]["role"] != "assistant":
+    with st.chat_message("assistant"):
+        with st.spinner("Thinking..."):
+            response = generate_response(prompt, hf_email, hf_pass) 
+            st.write(response) 
+    message = {"role": "assistant", "content": response}
+    st.session_state.messages.append(message)

login_test.py

@@ -0,0 +1,5 @@
+from hugchat import hugchat
+from hugchat.login import Login
+
+sign = Login("pax.dare@gmail.com", "SASuse57343983")
+cookies = sign.login()

requirements.txt

@@ -4,4 +4,4 @@ transformers
 langchain
 Cython
 torch
-hugchat
+hugchat==0.4.4