Update app.py

app.py

@@ -1,42 +1,194 @@
 import streamlit as st
-import pandas as pd
-import numpy as np
-
-st.title('Uber pickups in NYC')
-
-DATE_COLUMN = 'date/time'
-DATA_URL = ('https://s3-us-west-2.amazonaws.com/'
-            'streamlit-demo-data/uber-raw-data-sep14.csv.gz')
-
-@st.cache_data
-def load_data(nrows):
-    data = pd.read_csv(DATA_URL, nrows=nrows)
-    lowercase = lambda x: str(x).lower()
-    data.rename(lowercase, axis='columns', inplace=True)
-    data[DATE_COLUMN] = pd.to_datetime(data[DATE_COLUMN])
-    return data
-
-data_load_state = st.text('Loading data...')
-data = load_data(10000)
-data_load_state.text("Done! (using st.cache)")
-
-if st.checkbox('Show raw data'):
-    st.subheader('Raw data')
-    st.write(data)
-
-st.subheader('Number of pickups by hour')
-hist_values = np.histogram(data[DATE_COLUMN].dt.hour, bins=24, range=(0,24))[0]
-st.bar_chart(hist_values)
-
-# Some number in the range 0-23
-hour_to_filter = st.slider('hour', 0, 23, 17)
-filtered_data = data[data[DATE_COLUMN].dt.hour == hour_to_filter]
-
-st.subheader('Map of all pickups at %s:00' % hour_to_filter)
-st.map(filtered_data)
-
-uploaded_file = st.file_uploader("Choose a file")
-if uploaded_file is not None:
-    st.write(uploaded_file.name)
-    bytes_data = uploaded_file.getvalue()
-    st.write(len(bytes_data), "bytes")
+import requests
+import json
+import os
+import datetime
+from huggingface_hub import InferenceClient
+
+# Constants
+SPACE_URL = "https://z7svds7k42bwhhgm.us-east-1.aws.endpoints.huggingface.cloud"
+HF_API_KEY = os.getenv("HF_API_KEY")  # Retrieve the Hugging Face API key from system variables
+EOS_TOKEN = "<|end|>"
+CHAT_HISTORY_DIR = "chat_histories"
+IMAGE_PATH = "DubsChat.png"
+IMAGE_PATH_2 = "Reboot AI.png"
+DUBS_PATH = "Dubs.png"
+
+# Ensure the directory exists
+try:
+    os.makedirs(CHAT_HISTORY_DIR, exist_ok=True)
+except OSError as e:
+    st.error(f"Failed to create chat history directory: {e}")
+
+# Streamlit Configurations
+st.set_page_config(page_title="DUBSChat", page_icon=IMAGE_PATH, layout="wide")
+st.logo(IMAGE_PATH_2,size="large")
+
+# -------------------------
+#    Chat Template
+# -------------------------
+CHAT_TEMPLATE = """
+<|system|>
+You are a helpful assistant.<|end|>
+{history}
+<|user|>
+{user_input}<|end|>
+<|assistant|>
+"""
+
+def format_chat_template(history, user_input):
+    """
+    Formats the chat template by combining the chat history and user input.
+    """
+    return CHAT_TEMPLATE.format(history=history, user_input=user_input)
+
+# -------------------------
+#    Generate Chat History
+# -------------------------
+def format_chat_history(messages):
+    """
+    Converts the chat messages into a string compatible with the chat template.
+    Ensures no duplicate <|assistant|> tokens in the history.
+    """
+    history = ""
+    for message in messages:
+        if message["role"] == "user":
+            history += f"<|user|>{message['content']}<|end|>\n"
+        elif message["role"] == "assistant":
+            history += f"<|assistant|>{message['content']}<|end|>\n"
+    return history.strip()  # Remove any trailing newlines
+
+# -------------------------
+#    Utility Functions
+# -------------------------
+def save_chat_history(session_name, messages):
+    """
+    Save the chat history to a JSON file.
+    """
+    file_path = os.path.join(CHAT_HISTORY_DIR, f"{session_name}.json")
+    try:
+        with open(file_path, "w") as f:
+            json.dump(messages, f)
+    except IOError as e:
+        st.error(f"Failed to save chat history: {e}")
+
+
+def load_chat_history(file_name):
+    """
+    Load the chat history from a JSON file.
+    """
+    file_path = os.path.join(CHAT_HISTORY_DIR, file_name)
+    try:
+        with open(file_path, "r") as f:
+            return json.load(f)
+    except (FileNotFoundError, json.JSONDecodeError):
+        st.error("Failed to load chat history. Starting with a new session.")
+        return []
+
+
+def get_saved_sessions():
+    """
+    Get the list of saved chat sessions.
+    """
+    return [f.replace(".json", "") for f in os.listdir(CHAT_HISTORY_DIR) if f.endswith(".json")]
+
+# -------------------------
+#    Sidebar Configuration
+# -------------------------
+with st.sidebar:
+    if st.button("New Chat"):
+        st.session_state["messages"] = [
+            {"role": "system", "content": "You are Dubs, a helpful assistant created my RebootAI"},
+        ]
+        st.session_state["session_name"] = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
+        save_chat_history(st.session_state["session_name"], st.session_state["messages"])
+        st.success("Chat reset and new session started.")
+    
+    saved_sessions = get_saved_sessions()
+    if saved_sessions:
+        selected_session = st.radio("Past Sessions:", saved_sessions)
+        if st.button("Load Session"):
+            st.session_state["messages"] = load_chat_history(f"{selected_session}.json")
+            st.session_state["session_name"] = selected_session
+            st.success(f"Loaded session: {selected_session}")
+    else:
+        st.write("No past sessions available.")
+
+# -------------------------
+# Chat History Initialization
+# -------------------------
+if "messages" not in st.session_state:
+    st.session_state["messages"] = [
+        {"role": "system", "content": "You are Dubs, a helpful assistant created my RebootAI"}
+    ]
+if "session_name" not in st.session_state:
+    st.session_state["session_name"] = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
+
+# -------------------------
+#        Main Chat UI
+# -------------------------
+st.image(IMAGE_PATH, width=250)
+st.markdown("Empowering you with a Sustainable AI")
+
+# Display existing chat history
+for message in st.session_state["messages"]:
+    if message["role"] == "user":
+        st.chat_message("user").write(message["content"])
+    elif message["role"] == "assistant":
+        st.chat_message("assistant", avatar=DUBS_PATH).write(message["content"])
+
+client = InferenceClient(SPACE_URL, token=HF_API_KEY)
+
+# -------------------------
+#    Streaming Logic
+# -------------------------
+def stream_response(prompt_text):
+    """
+    Stream text from the HF Inference Endpoint using the InferenceClient.
+    Yields each partial chunk of text as it arrives.
+    """
+    gen_kwargs = {
+        "max_new_tokens": 1024,
+        "top_k": 30,
+        "top_p": 0.9,
+        "temperature": 0.2,
+        "repetition_penalty": 1.02,
+        "stop_sequences": ["<|end|>"]
+    }
+
+    stream = client.text_generation(prompt_text, stream=True, details=True, **gen_kwargs)
+
+    for response in stream:
+        if response.token.special:
+            continue
+        yield response.token.text
+
+# -------------------------
+#       User Input
+# -------------------------
+prompt = st.chat_input()
+
+if prompt:
+    # 1) Add the user's message to session state
+    st.session_state["messages"].append({"role": "user", "content": prompt})
+    st.chat_message("user").write(prompt)
+
+    # 2) Format chat history and user input for the template
+    chat_history = format_chat_history(st.session_state["messages"][:-1])  # Exclude the current user input
+    model_input = format_chat_template(chat_history, prompt)
+
+    # 3) Generate the assistant's response
+    with st.spinner("Dubs is thinking... Woof Woof! 🐾"):
+        msg = ""
+        with st.chat_message("assistant", avatar=DUBS_PATH):
+            response_stream = stream_response(model_input)
+            msg = st.write_stream(response_stream)
+
+    # 4) Add the assistant's response to session state
+    st.session_state["messages"].append({"role": "assistant", "content": msg})
+
+    # 5) Persist the updated chat history
+    save_chat_history(st.session_state["session_name"], st.session_state["messages"])
+
+
+