HF

.gitattributes

@@ -1,2 +1,8 @@
+<<<<<<< HEAD
 Trained[[:space:]]Model/bert_chatbot_model.pth filter=lfs diff=lfs merge=lfs -text
 Trained[[:space:]]Model/chatbot_model/model.safetensors filter=lfs diff=lfs merge=lfs -text
+=======
+Trained[[:space:]]Model/bert_chatbot_model.pth filter=lfs diff=lfs merge=lfs -text
+Trained[[:space:]]Model/chatbot_model/model.safetensors filter=lfs diff=lfs merge=lfs -text
+Images/Bot[[:space:]]Image.jpeg filter=lfs diff=lfs merge=lfs -text
+>>>>>>> hf/main

README.md

@@ -1,3 +1,4 @@
+<<<<<<< HEAD
 # **🤖 AI-Powered Chatbot using NLP**  
 
 ## **📌 Introduction**  
@@ -109,3 +110,18 @@ This project is **open-source** and free to use. Feel free to contribute!
 📌 **GitHub:** [Samarth4023](https://github.com/Samarth4023)  
 📌 **LinkedIn:** [Samarth Pujari](https://www.linkedin.com/in/samarth-pujari-328a1326a)  
 
+=======
+---
+license: apache-2.0
+title: 🧠AI Chatbot🤖
+sdk: streamlit
+emoji: 💻
+colorFrom: pink
+colorTo: purple
+thumbnail: >-
+  https://cdn-uploads.huggingface.co/production/uploads/6686260107019f3fe482ce08/AyL6xBZnErZdG1_ou4QgV.jpeg
+short_description: The System on Real Time Intent Recognition and Conversations
+---
+# Implementation of Chatbot using Natural Language Processing (NLP)
+Welcome!
+>>>>>>> hf/main

app.py

@@ -1,3 +1,4 @@
+<<<<<<< HEAD
 import streamlit as st
 import torch
 from transformers import BertTokenizer, BertForSequenceClassification
@@ -185,4 +186,193 @@ st.markdown("""
     .stChatMessageUser {background-color: #900C3F;}
     .stChatMessageAssistant {background-color: #900C3F;}
 </style>
+=======
+import streamlit as st
+import torch
+from transformers import BertTokenizer, BertForSequenceClassification
+import json
+import os
+import time
+import datetime
+import random
+from streamlit_extras.add_vertical_space import add_vertical_space
+from streamlit_extras.let_it_rain import rain
+from streamlit_modal import Modal
+
+# Streamlit UI
+st.set_page_config(page_title="ChatBot", layout="wide")
+
+# Load BERT Model & Tokenizer
+MODEL_PATH = "Trained Model/chatbot_model"  # Path where your model & tokenizer are saved
+
+# Set device
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Load model & tokenizer
+tokenizer = BertTokenizer.from_pretrained(MODEL_PATH)
+model = BertForSequenceClassification.from_pretrained(MODEL_PATH)
+model.to(device)
+model.eval()
+
+# Load intents
+with open("intents.json", "r") as file:
+    intents = json.load(file)
+
+intent_mapping = {i: intent['tag'] for i, intent in enumerate(intents)}
+
+# Chat history file
+CHAT_HISTORY_FILE = "chat_history.json"
+
+# Load Chat History (Ensuring Persistence)
+def load_chat_history():
+    if os.path.exists(CHAT_HISTORY_FILE):
+        with open(CHAT_HISTORY_FILE, "r") as file:
+            return json.load(file)
+    return []
+
+# Save Chat History
+def save_chat_history():
+    with open(CHAT_HISTORY_FILE, "w") as file:
+        json.dump(st.session_state.chat_history, file)
+
+# Initialize session state variables
+if "chat_history" not in st.session_state:
+    st.session_state.chat_history = load_chat_history()
+if "show_history" not in st.session_state:
+    st.session_state.show_history = False
+if "show_about" not in st.session_state:
+    st.session_state.show_about = False
+if "chat_input" not in st.session_state:
+    st.session_state.chat_input = ""
+
+def predict_intent(text):
+    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
+    with torch.no_grad():
+        outputs = model(**inputs)
+
+    predicted_index = torch.argmax(outputs.logits, dim=1).item()
+    return intent_mapping.get(predicted_index, "unknown")
+
+def get_response(intent_label):
+    for intent in intents:
+        if intent_label == intent['tag']:
+            return random.choice(intent['responses'])
+    return "I'm not sure how to respond to that."
+
+# Sidebar menu
+with st.sidebar:
+    st.image("Images/Bot Image.jpeg", use_container_width=True)
+    st.title("💬 Chatbot Menu")
+    add_vertical_space(1)
+
+    if st.button("🆕 New Chat"):
+        st.session_state.chat_input = ""
+        save_chat_history()
+
+    if st.button("📜 Chat History"):
+        st.session_state.show_history = True
+
+    if st.button("ℹ️ About"):
+        st.session_state.show_about = True
+    st.caption("🚀 Built with ❤️ by AI Enthusiast")
+
+# Modal Windows (Chat History & About)
+history_modal = Modal("📜Chat History", key="history_modal")
+about_modal = Modal("ℹ️About", key="about_modal")
+
+# Display chat history without affecting the chat window
+if st.session_state.show_history:
+    with history_modal.container():
+        if st.session_state.chat_history:
+            for chat in st.session_state.chat_history[-10:]:  # Show last 10 messages
+                st.write(chat)
+        else:
+            st.info("No chat history available.")
+
+        # Clear History Button
+        if st.button("🗑 Clear History", key="clear_history"):
+            st.session_state.chat_history = []  # Clear session history
+            if os.path.exists(CHAT_HISTORY_FILE):
+                os.remove(CHAT_HISTORY_FILE)  # Delete saved file
+            st.rerun()  # Refresh UI to reflect changes
+
+        # Close Modal Button
+        if st.button("Close", key="close_history"):
+            st.session_state.show_history = False
+            st.rerun()
+
+if st.session_state.show_about:
+    with about_modal.container():
+        st.info("""This chatbot is powered by a deep learning approach using Hugging Face Transformers and a BERT model for intent recognition. 
+It understands user queries by predicting intent and responding with predefined answers based on a trained dataset. 
+
+✅ Natural Language Processing (NLP)​
+                
+🔹 Transformers (Hugging Face) 🤖 – BERT-based intent classification​
+
+✅ Deep Learning​
+                
+🔹 PyTorch 🔥 – Model training & fine-tuning
+""")
+        
+        if st.button("Close", key="close_about"):
+            st.session_state.show_about = False  # Close modal without resetting UI
+            st.rerun()
+
+# Chat UI
+st.title("🤖 AI Chatbot")
+st.markdown("### Talk to me, I'm listening...")
+
+rain(emoji="💬", font_size=10, falling_speed=5, animation_length="infinite")
+rain(emoji="❄️", font_size=10, falling_speed=5, animation_length="infinite")
+
+# Chat Input with Enter Button
+with st.form("chat_form", clear_on_submit=True):
+    chat_input = st.text_input("You:", key="chat_input")
+    submit_button = st.form_submit_button("Enter")
+
+if submit_button and chat_input:
+    intent_label = predict_intent(chat_input)
+    response = get_response(intent_label)
+    chat_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+
+    # Append messages to chat history (User & Bot)
+    st.session_state.chat_history.append(f"[{chat_time}] You: {chat_input}")
+    st.session_state.chat_history.append(f"[{chat_time}] Bot: {response}")
+
+    # Save chat history persistently
+    save_chat_history()
+
+    # Display conversation immediately
+    with st.chat_message("user"):
+        st.markdown(f"**You:** {chat_input}")
+        time.sleep(0.5)
+    with st.chat_message("assistant"):
+        st.markdown(f"**Bot:** {response}")
+
+# Style customization
+st.markdown("""
+<style>
+
+    /* Fix Modal Height & Enable Scroll */
+    div[data-modal-container="true"] {
+        position: fixed !important;
+        top: 5% !important;  /* Adjust this value to position it higher */
+        color: #000000;
+        max-height: 1000px !important;  /* Set max height */
+    }
+
+    /* Ensure the close button stays visible */
+    div[data-modal-container="true"] button {
+        position: sticky;
+        bottom: 10px;
+        background-color: #000000;
+        color: white;
+    }
+            
+    .stChatMessage {padding: 10px; border-radius: 10px; background-color: #900C3F;}
+    .stChatMessageUser {background-color: #900C3F;}
+    .stChatMessageAssistant {background-color: #900C3F;}
+</style>
+>>>>>>> hf/main
 """, unsafe_allow_html=True)

requirements.txt

@@ -1,5 +1,13 @@
+<<<<<<< HEAD
 streamlit
 torch
 transformers
 streamlit_extras
 streamlit_modal
+=======
+streamlit
+torch
+transformers
+streamlit_extras
+streamlit_modal
+>>>>>>> hf/main