Create app.bak

app.bak

@@ -0,0 +1,313 @@
+import streamlit as st
+import requests
+from bertopic import BERTopic
+from sentence_transformers import SentenceTransformer
+import numpy as np
+from sklearn.metrics.pairwise import cosine_similarity
+import pandas as pd
+import plotly.graph_objects as go
+from datetime import datetime
+import json
+from collections import deque
+from datasets import load_dataset
+
+class BERTopicChatbot:
+    
+    #Initialize chatbot with a Hugging Face dataset
+    #dataset_name: name of the dataset on Hugging Face (e.g., 'vietnam/legal')
+    #text_column: name of the column containing the text data
+    #split: which split of the dataset to use ('train', 'test', 'validation')
+    #max_samples: maximum number of samples to use (to manage memory)
+    
+    def __init__(self, dataset_name, text_column, split="train", max_samples=10000):
+        # Initialize BERT sentence transformer
+        self.sentence_model = SentenceTransformer('all-MiniLM-L6-v2')
+        # Load dataset from Hugging Face
+        try:
+            dataset = load_dataset(dataset_name, split=split)
+            # Convert to pandas DataFrame and sample if necessary
+            if len(dataset) > max_samples:
+                dataset = dataset.shuffle(seed=42).select(range(max_samples))
+            
+            self.df = dataset.to_pandas()
+            
+            # Ensure text column exists
+            if text_column not in self.df.columns:
+                raise ValueError(f"Column '{text_column}' not found in dataset. Available columns: {self.df.columns}")
+            
+            self.documents = self.df[text_column].tolist()
+            
+            # Create and train BERTopic model
+            self.topic_model = BERTopic(embedding_model=self.sentence_model)
+            self.topics, self.probs = self.topic_model.fit_transform(self.documents)
+            
+            # Create document embeddings for similarity search
+            self.doc_embeddings = self.sentence_model.encode(self.documents)
+            
+            # Initialize metrics storage
+            self.metrics_history = {
+                'similarities': deque(maxlen=100),
+                'response_times': deque(maxlen=100),
+                'token_counts': deque(maxlen=100),
+                'topics_accessed': {}
+            }
+            
+            # Store dataset info
+            self.dataset_info = {
+                'name': dataset_name,
+                'split': split,
+                'total_documents': len(self.documents),
+                'topics_found': len(set(self.topics))
+            }
+            
+        except Exception as e:
+            st.error(f"Error loading dataset: {str(e)}")
+            raise
+
+    def get_metrics_visualizations(self):
+        """Generate visualizations for chatbot metrics"""
+        # Similarity trend
+        fig_similarity = go.Figure()
+        fig_similarity.add_trace(go.Scatter(
+            y=list(self.metrics_history['similarities']),
+            mode='lines+markers',
+            name='Similarity Score'
+        ))
+        fig_similarity.update_layout(
+            title='Response Similarity Trend',
+            yaxis_title='Similarity Score',
+            xaxis_title='Query Number'
+        )
+    
+        # Response time trend
+        fig_response_time = go.Figure()
+        fig_response_time.add_trace(go.Scatter(
+            y=list(self.metrics_history['response_times']),
+            mode='lines+markers',
+            name='Response Time'
+        ))
+        fig_response_time.update_layout(
+            title='Response Time Trend',
+            yaxis_title='Time (seconds)',
+            xaxis_title='Query Number'
+        )
+    
+        # Token usage trend
+        fig_tokens = go.Figure()
+        fig_tokens.add_trace(go.Scatter(
+            y=list(self.metrics_history['token_counts']),
+            mode='lines+markers',
+            name='Token Count'
+        ))
+        fig_tokens.update_layout(
+            title='Token Usage Trend',
+            yaxis_title='Number of Tokens',
+            xaxis_title='Query Number'
+        )
+    
+        # Topics accessed pie chart
+        labels = list(self.metrics_history['topics_accessed'].keys())
+        values = list(self.metrics_history['topics_accessed'].values())
+        fig_topics = go.Figure(data=[go.Pie(labels=labels, values=values)])
+        fig_topics.update_layout(title='Topics Accessed Distribution')
+    
+        # Make all figures responsive
+        for fig in [fig_similarity, fig_response_time, fig_tokens, fig_topics]:
+            fig.update_layout(
+                autosize=True,
+                margin=dict(l=20, r=20, t=40, b=20),
+                height=300
+            )
+    
+        return fig_similarity, fig_response_time, fig_tokens, fig_topics
+    
+    def get_most_similar_document(self, query, top_k=3):
+        # Encode the query
+        query_embedding = self.sentence_model.encode([query])[0]
+        
+        # Calculate similarities
+        similarities = cosine_similarity([query_embedding], self.doc_embeddings)[0]
+        
+        # Get top k most similar documents
+        top_indices = similarities.argsort()[-top_k:][::-1]
+        
+        return [self.documents[i] for i in top_indices], similarities[top_indices]
+
+    def get_response(self, user_query):
+        try:
+            start_time = datetime.now()
+            
+            # Get most similar documents
+            similar_docs, similarities = self.get_most_similar_document(user_query)
+            
+            # Get topic for the query
+            query_topic, _ = self.topic_model.transform([user_query])
+            
+            # Track topic access
+            topic_id = str(query_topic[0])
+            self.metrics_history['topics_accessed'][topic_id] = \
+                self.metrics_history['topics_accessed'].get(topic_id, 0) + 1
+            
+            # If similarity is too low, return a default response
+            if max(similarities) < 0.5:
+                response = "Xin lỗi, tôi không có đủ thông tin để trả lời câu hỏi này một cách chính xác."
+            else:
+                response = similar_docs[0]
+            
+            # Track metrics
+            end_time = datetime.now()
+            self.metrics_history['similarities'].append(float(max(similarities)))
+            self.metrics_history['response_times'].append((end_time - start_time).total_seconds())
+            self.metrics_history['token_counts'].append(len(response.split()))
+            
+            metrics = {
+                'similarity': float(max(similarities)),
+                'response_time': (end_time - start_time).total_seconds(),
+                'tokens': len(response.split()),
+                'topic': topic_id
+            }
+            
+            return response, metrics
+            
+        except Exception as e:
+            return f"Error processing query: {str(e)}", {'error': str(e)}
+    
+    def get_dataset_info(self):
+        #Return information about the loaded dataset and metrics
+        try:
+            return {
+                'dataset_info': self.dataset_info,
+                'metrics': {
+                    'avg_similarity': np.mean(list(self.metrics_history['similarities'])) if self.metrics_history['similarities'] else 0,
+                    'avg_response_time': np.mean(list(self.metrics_history['response_times'])) if self.metrics_history['response_times'] else 0,
+                    'total_tokens': sum(self.metrics_history['token_counts']),
+                    'topics_accessed': self.metrics_history['topics_accessed']
+                }
+            }
+        except Exception as e:
+            return {
+                'error': str(e),
+                'dataset_info': None,
+                'metrics': None
+            }
+            
+@st.cache_resource
+def initialize_chatbot(dataset_name, text_column, split="train", max_samples=10000):
+    return BERTopicChatbot(dataset_name, text_column, split, max_samples)
+
+def main():
+    st.title("🤖 Trợ Lý AI - BERTopic")
+    st.caption("Trò chuyện với chúng mình nhé!")
+
+    # Dataset selection sidebar
+    with st.sidebar:
+        st.header("Dataset Configuration")
+        dataset_name = st.text_input(
+            "Hugging Face Dataset Name",
+            value="Kanakmi/mental-disorders",
+            help="Enter the name of a dataset from Hugging Face (e.g., 'Kanakmi/mental-disorders')"
+        )
+        text_column = st.text_input(
+            "Text Column Name",
+            value="text",
+            help="Enter the name of the column containing the text data"
+        )
+        split = st.selectbox(
+            "Dataset Split",
+            options=["train", "test", "validation"],
+            index=0
+        )
+        max_samples = st.number_input(
+            "Maximum Samples",
+            min_value=100,
+            max_value=100000,
+            value=10000,
+            step=1000,
+            help="Maximum number of samples to load from the dataset"
+        )
+        
+        if st.button("Load Dataset"):
+            with st.spinner("Loading dataset and initializing model..."):
+                try:
+                    st.session_state.chatbot = initialize_chatbot(
+                        dataset_name, text_column, split, max_samples
+                    )
+                    st.success("Dataset loaded successfully!")
+                except Exception as e:
+                    st.error(f"Error loading dataset: {str(e)}")
+
+    # Initialize session state variables if they don't exist
+    if 'chatbot' not in st.session_state:
+        st.session_state.chatbot = None
+    
+    if 'messages' not in st.session_state:
+        st.session_state.messages = []
+
+    # Create tabs for chat and metrics
+    chat_tab, metrics_tab = st.tabs(["Chat", "Metrics"])
+
+    with chat_tab:
+        # Display existing messages
+        for message in st.session_state.messages:
+            with st.chat_message(message["role"]):
+                st.markdown(message["content"])
+
+        # Only show chat input if chatbot is initialized
+        if st.session_state.chatbot is not None:
+            if prompt := st.chat_input("Hãy nói gì đó..."):
+                # Add user message
+                st.session_state.messages.append({"role": "user", "content": prompt})
+                with st.chat_message("user"):
+                    st.markdown(prompt)
+
+                # Get chatbot response
+                response, metrics = st.session_state.chatbot.get_response(prompt)
+
+                # Add assistant response
+                with st.chat_message("assistant"):
+                    st.markdown(response)
+                    with st.expander("Response Metrics"):
+                        st.json(metrics)
+                
+                st.session_state.messages.append({"role": "assistant", "content": response})
+        else:
+            st.info("Please load a dataset first to start chatting.")
+
+    with metrics_tab:
+        if st.session_state.chatbot is not None:
+            try:
+                # Get visualizations from session state chatbot
+                fig_similarity, fig_response_time, fig_tokens, fig_topics = st.session_state.chatbot.get_metrics_visualizations()
+                
+                col1, col2 = st.columns(2)
+                with col1:
+                    st.plotly_chart(fig_similarity, use_container_width=True)
+                    st.plotly_chart(fig_tokens, use_container_width=True)
+                
+                with col2:
+                    st.plotly_chart(fig_response_time, use_container_width=True)
+                    st.plotly_chart(fig_topics, use_container_width=True)
+
+                # Display statistics
+                st.subheader("Overall Statistics")
+                metrics_history = st.session_state.chatbot.metrics_history
+                if len(metrics_history['similarities']) > 0:
+                    stats_col1, stats_col2, stats_col3 = st.columns(3)
+                    with stats_col1:
+                        st.metric("Avg Similarity", 
+                                 f"{np.mean(list(metrics_history['similarities'])):.3f}")
+                    with stats_col2:
+                        st.metric("Avg Response Time", 
+                                 f"{np.mean(list(metrics_history['response_times'])):.3f}s")
+                    with stats_col3:
+                        st.metric("Total Tokens Used", 
+                                 sum(metrics_history['token_counts']))
+                else:
+                    st.info("No chat history available yet. Start a conversation to see metrics.")
+            except Exception as e:
+                st.error(f"Error displaying metrics: {str(e)}")
+        else:
+            st.info("Please load a dataset first to view metrics.")
+
+if __name__ == "__main__":
+    main()