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c3f8261
1
Parent(s):
33777fa
updated code
Browse files
app.py
CHANGED
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@@ -1,103 +1,26 @@
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# import gradio as gr
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# import joblib
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# from src.preprocess import clean_text
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# import datetime
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# # Load model & responses
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# model = joblib.load("models/lms_chatbot.joblib")
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# responses = joblib.load("models/responses.joblib")
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# # Keep conversation history
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# history = []
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# def chatbot_response(user_input):
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# if not user_input.strip():
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# return ""
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# # Add user message
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# timestamp = datetime.datetime.now().strftime("%H:%M")
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# history.append({
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# "sender": "You",
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# "message": user_input,
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# "time": timestamp,
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# "color": "#DCF8C6",
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# "align": "right"
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# })
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# # Bot prediction
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# tag = model.predict([clean_text(user_input)])[0]
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# bot_reply = responses.get(tag, ["Sorry, I don't understand."])[0]
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# # Add bot message
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# timestamp = datetime.datetime.now().strftime("%H:%M")
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# history.append({
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# "sender": "Bot",
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# "message": bot_reply,
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# "time": timestamp,
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# "color": "#FFFFFF",
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# "align": "left"
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# })
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# # Render chat
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# return render_chat()
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# def render_chat():
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# chat_html = """
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# <div style="font-family:Helvetica, Arial; background:#F0F0F0; padding:15px; height:400px; overflow-y:auto; border-radius:10px; border:1px solid #ccc;">
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# """
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# for msg in history:
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# chat_html += f"""
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# <div style="text-align:{msg['align']}; margin:8px 0;">
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# <div style="display:inline-block; background:{msg['color']}; padding:10px 15px; border-radius:20px; max-width:70%; box-shadow:0 2px 5px rgba(0,0,0,0.2);">
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# {msg['message']}<br>
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# <span style="font-size:10px; color:gray; float:right;">{msg['time']}</span>
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# </div>
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# </div>
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# """
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# chat_html += "</div>"
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# return chat_html
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# # Gradio interface
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# demo = gr.Interface(
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# fn=chatbot_response,
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# inputs=gr.Textbox(lines=2, placeholder="Type your message here...", label="Your Message"),
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# outputs=gr.HTML(label="Chat"),
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# title="π’ LMS Chatbot",
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# description="Ask anything about your LMS. Automatic reply with chat bubbles and timestamps!"
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# )
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# if __name__ == "__main__":
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# demo.launch()
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# app.py
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import streamlit as st
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import joblib
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from src.preprocess import clean_text
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import
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# Load trained model & responses
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model = joblib.load("models/lms_chatbot.joblib")
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responses = joblib.load("models/responses.joblib")
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# Initialize session state
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if "messages" not in st.session_state:
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st.session_state.messages = []
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def chatbot_response(user_input):
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"""Predict tag and generate response."""
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if not user_input.strip():
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return ""
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tag = model.predict([clean_text(user_input)])[0]
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bot_reply = responses.get(tag, ["Sorry, I don't understand."])[0]
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return bot_reply
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# Streamlit UI
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st.set_page_config(page_title="π’ LMS Chatbot", page_icon="π€")
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st.title("π’ LMS Chatbot")
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st.markdown("Ask anything about your LMS and get automated responses!")
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# User input
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user_input = st.text_input("Type your message here:", key="input")
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# Append user message
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st.session_state.messages.append({"sender": "user", "content": user_input})
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#
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bot_reply = chatbot_response(user_input)
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# Display chat messages
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for msg in st.session_state.messages:
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else:
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with st.chat_message("assistant"):
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st.write(msg["content"])
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import streamlit as st
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import joblib
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from src.preprocess import clean_text
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import time
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# Load trained model & responses
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model = joblib.load("models/lms_chatbot.joblib")
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responses = joblib.load("models/responses.joblib")
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# Initialize session state
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if "messages" not in st.session_state:
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st.session_state.messages = []
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st.set_page_config(page_title="π’ LMS Chatbot", page_icon="π€")
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st.title("π’ LMS Chatbot")
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st.markdown("Ask anything about your LMS and get automated responses!")
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# Function to generate bot response
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def chatbot_response(user_input):
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tag = model.predict([clean_text(user_input)])[0]
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bot_reply = responses.get(tag, ["Sorry, I don't understand."])[0]
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return bot_reply
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# User input
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user_input = st.text_input("Type your message here:", key="input")
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# Append user message
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st.session_state.messages.append({"sender": "user", "content": user_input})
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# Typing animation for bot
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bot_reply = chatbot_response(user_input)
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display_text = ""
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st.session_state.messages.append({"sender": "bot", "content": ""}) # placeholder
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bot_index = len(st.session_state.messages) - 1
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for char in bot_reply:
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display_text += char
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st.session_state.messages[bot_index]["content"] = display_text
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time.sleep(0.03) # adjust typing speed
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st.experimental_rerun() # refresh chat to show animation
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# Display chat messages
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for msg in st.session_state.messages:
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else:
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with st.chat_message("assistant"):
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st.write(msg["content"])
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