Create app.py

app.py

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+import streamlit as st
+from hugchat import hugchat
+from hugchat.login import Login
+
+from huggingface_hub import InferenceClient
+client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+
+
+# 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(messages, email, passwd):
+    for message in client.chat_completion(
+        messages,
+        max_tokens=500,
+        stream=True,
+        temperature=0.7,
+        top_p=0.9,
+    ):
+        token = message.choices[0].delta.content
+
+        response += token
+        yield response
+
+# def generate_response(prompt_input, email, passwd):
+#     # Hugging Face Login
+#     cookie_path_dir = "./cookies/"
+#     sign = Login(email, passwd)
+#     cookies = sign.login(cookie_dir_path=cookie_path_dir, save_cookies=True)
+#     # Create ChatBot                        
+#     chatbot = hugchat.ChatBot(cookies=cookies.get_dict())
+#     return chatbot.chat(prompt_input)
+
+# # Function for generating LLM response based on "HuggingFaceH4/zephyr-7b-beta"
+# def respond(message, history: list[tuple[str, str]], system_message, max_tokens, temperature, top_p,):
+#     messages = [{"role": "system", "content": system_message}]
+
+#     for val in history:
+#         if val[0]:
+#             messages.append({"role": "user", "content": val[0]})
+#         if val[1]:
+#             messages.append({"role": "assistant", "content": val[1]})
+
+#     messages.append({"role": "user", "content": message})
+
+#     response = ""
+
+#     for message in client.chat_completion(
+#         messages,
+#         max_tokens=max_tokens,
+#         stream=True,
+#         temperature=temperature,
+#         top_p=top_p,
+#     ):
+#         token = message.choices[0].delta.content
+
+#         response += token
+#         yield response
+
+# 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)