Update app.py

app.py

@@ -1,206 +1,206 @@
-import streamlit as st
-from transformers import AutoTokenizer,  AutoModelForSequenceClassification, pipeline,AutoModelForSeq2SeqLM
-from sqlalchemy import create_engine, Column, Integer, String, DateTime,Text
-from sqlalchemy.orm import sessionmaker
-from sqlalchemy.ext.declarative import declarative_base
-from datetime import datetime
-from sklearn.feature_extraction.text import TfidfVectorizer
-from sklearn.linear_model import LogisticRegression
-import torch
-
-
-st.title("Simple Chatbot with persistent memory (mysql)(Flan -T5)")
-
-
-# database setup mysql
-
-Base = declarative_base()
-
-class Conversation(Base):
-    __tablename__ = "conversations"
-    id = Column(Integer , primary_key = True ,autoincrement= True)
-    user_input = Column(Text, nullable = False)
-    chatbot_response = Column(Text, nullable = False)
-    timestamp = Column(DateTime, nullable = False)
-# creating engine to connect to mysql 
-
-DATABASE_URL = "sqlite:///chatbot.db"  # SQLite database file
-engine = create_engine(DATABASE_URL, connect_args={"check_same_thread": False})  
-Base.metadata.create_all(engine)Base.metadata.create_all(engine)
-
-Session = sessionmaker(bind=engine)
-    
-
-# LOADING THE MODEL AND TOKENIZER
-@st.cache_resource
-def load_model():
-    model_name = "google/flan-t5-base"
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-    model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
-    return tokenizer, model
-    
-
-tokenizer, model = load_model()
-
-# intent detection setup
-intents = {
-     "greeting": ["hello", "hi", "hey", "good morning", "good afternoon"],
-    "farewell": ["bye", "goodbye", "see you later", "farewell"],
-    "general": ["how are you?", "tell me a joke", "what is the weather?","who am i?","do you recognize me?"],
-    "about_me": ["what is your name?","tell me about yourself", "who are you","you?","Murphy?"],
-    "search_web": ["search for", "find", "what is", "look up", "search the web for", "google", "find information about", "show me", "give me info on", "tell me about"]
-}
-all_texts = []
-all_labels = []
-
-for label, texts in intents.items():
-    all_texts.extend(texts)
-    all_labels.extend([label]*len(texts))
-
-# vectorizer 
-
-vectorizer = TfidfVectorizer()
-X = vectorizer.fit_transform(all_texts)
-
-
-# classsifier
-
-classifier = LogisticRegression()
-classifier.fit(X, all_labels)
-
-# intention detection function 
-def detect_intent(user_input):
-    user_input_vectorized = vectorizer.transform([user_input])
-    intent = classifier.predict(user_input_vectorized)[0]
-    return intent
-# Few-shot examples
-few_shot_examples = [
-    "User: What is the weather like today?",
-    "Chatbot: I'm sorry, I cannot provide real time information.",
-    "User: Tell me a joke.",
-    "Chatbot: Why don't scientists trust atoms? Because they make up everything!",
-]
-
-if 'conversation_history' not in st.session_state:
-    st.session_state.conversation_history = []
-
-    # sentiment analysis setup
-sentiment_pipeline = pipeline("sentiment-analysis")
-def get_sentiment(text):
-    result = sentiment_pipeline(text)[0]
-    return result["label"], result["score"]
-
-sentiment_threshold = 0.8
-
-
-# sumarization setup
-@st.cache_resource
-def load_summarizer():
-    return pipeline("summarization", model="facebook/bart-large-cnn")
-
-summarizer = load_summarizer()
-
-
-# summarizer = pipeline("summarization")
-
-def summarize_history(history):
-    text = "\n".join(history)
-    summary = summarizer(text, max_length=150, min_length=30, do_sample=False)[0]['summary_text']
-    return summary
-
-user_input = st.text_input("You:", key="user_input_1")
-
-# transformer-based intent detection setup
-intent_model_name = "distilbert-base-uncased" 
-intent_tokenizer = AutoTokenizer.from_pretrained(intent_model_name)
-intent_model = AutoModelForSequenceClassification.from_pretrained(intent_model_name, num_labels=len(intents))
-
-# Load the current model into the GPU if available.
-# Load the current model into the GPU if available.
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-intent_model.to(device)  # Move the intent detection model to the correct device
-model.to(device)  #  move the flan to same device 
-
-def detect_intent_transformer(user_input):
-    inputs = intent_tokenizer(user_input, return_tensors="pt").to(device)
-    with torch.no_grad():
-        outputs = intent_model(**inputs)
-    probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
-    predicted_class = torch.argmax(probabilities, dim=-1).item()
-    return list(intents.keys())[predicted_class]
-
-
-if user_input:
-    session = Session()
-    try:
-        intent = detect_intent_transformer(user_input) #Use the transformer.
-        # FOR SENTIMENT
-        sentiment_label, sentiment_score = get_sentiment(user_input)
-        if intent == "greeting":
-            response = "Hello there! I'm Murphy, developed by Mr.Abhishek"
-        elif intent == "farewell":
-            response = "Goodbye!"
-        elif intent == "about_me":
-            response = "I'm Murphy, developed by Abhishek, i can learn by myself and i'm feeling good right now, seems like i am alive hehe!"
-        # elif intent == "search_web":
-        #     response = "Web search is disabled."
-        else:
-            
-            # Check if history needs summarization
-            if len(st.session_state.conversation_history) > 10:
-                try:
-                    summary = summarize_history(st.session_state.conversation_history)
-                    st.session_state.conversation_history = [summary]
-                except Exception as e:
-                    st.write(f"Summarization failed: {e}")
-            # construct few-shot prompt
-            few_shot_context = "\n".join(few_shot_examples)
-            # construct contextual prompt
-            context = "\n".join(st.session_state.conversation_history)
-            prompt = f"{few_shot_context}\n{context}\nUser: {user_input}\nChatbot:"
-            inputs = tokenizer(user_input, return_tensors="pt").to(device)
-            outputs = model.generate(** inputs, max_length = 50 , pad_token_id = tokenizer.eos_token_id)
-            response = tokenizer.decode(outputs[0], skip_special_tokens = True)
-
-# modifyinh responseds on the sentiment
-        if sentiment_score > sentiment_threshold:
-            if sentiment_label == "NEGATIVE":
-                response = f"I sense you're feeling a bit down. {response}"
-            elif sentiment_label == "POSITIVE":
-                response = f"I'm glad you're in a good mood! {response}"
-
-        # now i am storing conversations in the database
-        conversation = Conversation( user_input = user_input, chatbot_response = response, timestamp = datetime.now())
-        session.add(conversation)
-        session.commit()
-
-
-        # displaying the response
-        st.write(f"You: {user_input}")
-        st.write(f"Murphy:{response}")
-  
-# update the conversation history 
-
-        st.session_state.conversation_history.append(f"User: {user_input}")
-        st.session_state.conversation_history.append(f"Chatbot: {response}")
-
-    except Exception as e:
-        st.write(f"An error occurred: {e}")
-        session.rollback()
-    finally:
-        session.close()
-
-
-# dispaly the conversation from history 
-session = Session()
-try:
-    conversations = session.query(Conversation).all()
-    if conversations:
-        st.subheader("Conversations History")
-        for conv in conversations:
-            st.write(f"You: {conv.user_input}")
-            st.write(f"Murphy: {conv.chatbot_response}")
-except Exception as e:
-    st.write(f"Error retrieving conversations: {e}")
-finally:
+import streamlit as st
+from transformers import AutoTokenizer,  AutoModelForSequenceClassification, pipeline,AutoModelForSeq2SeqLM
+from sqlalchemy import create_engine, Column, Integer, String, DateTime,Text
+from sqlalchemy.orm import sessionmaker
+from sqlalchemy.ext.declarative import declarative_base
+from datetime import datetime
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.linear_model import LogisticRegression
+import torch
+
+
+st.title("Simple Chatbot with persistent memory (mysql)(Flan -T5)")
+
+
+# database setup mysql
+
+Base = declarative_base()
+
+class Conversation(Base):
+    __tablename__ = "conversations"
+    id = Column(Integer , primary_key = True ,autoincrement= True)
+    user_input = Column(Text, nullable = False)
+    chatbot_response = Column(Text, nullable = False)
+    timestamp = Column(DateTime, nullable = False)
+# creating engine to connect to mysql 
+
+DATABASE_URL = "sqlite:///chatbot.db"  # SQLite database file
+engine = create_engine(DATABASE_URL, connect_args={"check_same_thread": False})  
+Base.metadata.create_all(engine) 
+
+Session = sessionmaker(bind=engine)
+    
+
+# LOADING THE MODEL AND TOKENIZER
+@st.cache_resource
+def load_model():
+    model_name = "google/flan-t5-base"
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+    return tokenizer, model
+    
+
+tokenizer, model = load_model()
+
+# intent detection setup
+intents = {
+     "greeting": ["hello", "hi", "hey", "good morning", "good afternoon"],
+    "farewell": ["bye", "goodbye", "see you later", "farewell"],
+    "general": ["how are you?", "tell me a joke", "what is the weather?","who am i?","do you recognize me?"],
+    "about_me": ["what is your name?","tell me about yourself", "who are you","you?","Murphy?"],
+    "search_web": ["search for", "find", "what is", "look up", "search the web for", "google", "find information about", "show me", "give me info on", "tell me about"]
+}
+all_texts = []
+all_labels = []
+
+for label, texts in intents.items():
+    all_texts.extend(texts)
+    all_labels.extend([label]*len(texts))
+
+# vectorizer 
+
+vectorizer = TfidfVectorizer()
+X = vectorizer.fit_transform(all_texts)
+
+
+# classsifier
+
+classifier = LogisticRegression()
+classifier.fit(X, all_labels)
+
+# intention detection function 
+def detect_intent(user_input):
+    user_input_vectorized = vectorizer.transform([user_input])
+    intent = classifier.predict(user_input_vectorized)[0]
+    return intent
+# Few-shot examples
+few_shot_examples = [
+    "User: What is the weather like today?",
+    "Chatbot: I'm sorry, I cannot provide real time information.",
+    "User: Tell me a joke.",
+    "Chatbot: Why don't scientists trust atoms? Because they make up everything!",
+]
+
+if 'conversation_history' not in st.session_state:
+    st.session_state.conversation_history = []
+
+    # sentiment analysis setup
+sentiment_pipeline = pipeline("sentiment-analysis")
+def get_sentiment(text):
+    result = sentiment_pipeline(text)[0]
+    return result["label"], result["score"]
+
+sentiment_threshold = 0.8
+
+
+# sumarization setup
+@st.cache_resource
+def load_summarizer():
+    return pipeline("summarization", model="facebook/bart-large-cnn")
+
+summarizer = load_summarizer()
+
+
+# summarizer = pipeline("summarization")
+
+def summarize_history(history):
+    text = "\n".join(history)
+    summary = summarizer(text, max_length=150, min_length=30, do_sample=False)[0]['summary_text']
+    return summary
+
+user_input = st.text_input("You:", key="user_input_1")
+
+# transformer-based intent detection setup
+intent_model_name = "distilbert-base-uncased" 
+intent_tokenizer = AutoTokenizer.from_pretrained(intent_model_name)
+intent_model = AutoModelForSequenceClassification.from_pretrained(intent_model_name, num_labels=len(intents))
+
+# Load the current model into the GPU if available.
+# Load the current model into the GPU if available.
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+intent_model.to(device)  # Move the intent detection model to the correct device
+model.to(device)  #  move the flan to same device 
+
+def detect_intent_transformer(user_input):
+    inputs = intent_tokenizer(user_input, return_tensors="pt").to(device)
+    with torch.no_grad():
+        outputs = intent_model(**inputs)
+    probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
+    predicted_class = torch.argmax(probabilities, dim=-1).item()
+    return list(intents.keys())[predicted_class]
+
+
+if user_input:
+    session = Session()
+    try:
+        intent = detect_intent_transformer(user_input) #Use the transformer.
+        # FOR SENTIMENT
+        sentiment_label, sentiment_score = get_sentiment(user_input)
+        if intent == "greeting":
+            response = "Hello there! I'm Murphy, developed by Mr.Abhishek"
+        elif intent == "farewell":
+            response = "Goodbye!"
+        elif intent == "about_me":
+            response = "I'm Murphy, developed by Abhishek, i can learn by myself and i'm feeling good right now, seems like i am alive hehe!"
+        # elif intent == "search_web":
+        #     response = "Web search is disabled."
+        else:
+            
+            # Check if history needs summarization
+            if len(st.session_state.conversation_history) > 10:
+                try:
+                    summary = summarize_history(st.session_state.conversation_history)
+                    st.session_state.conversation_history = [summary]
+                except Exception as e:
+                    st.write(f"Summarization failed: {e}")
+            # construct few-shot prompt
+            few_shot_context = "\n".join(few_shot_examples)
+            # construct contextual prompt
+            context = "\n".join(st.session_state.conversation_history)
+            prompt = f"{few_shot_context}\n{context}\nUser: {user_input}\nChatbot:"
+            inputs = tokenizer(user_input, return_tensors="pt").to(device)
+            outputs = model.generate(** inputs, max_length = 50 , pad_token_id = tokenizer.eos_token_id)
+            response = tokenizer.decode(outputs[0], skip_special_tokens = True)
+
+# modifyinh responseds on the sentiment
+        if sentiment_score > sentiment_threshold:
+            if sentiment_label == "NEGATIVE":
+                response = f"I sense you're feeling a bit down. {response}"
+            elif sentiment_label == "POSITIVE":
+                response = f"I'm glad you're in a good mood! {response}"
+
+        # now i am storing conversations in the database
+        conversation = Conversation( user_input = user_input, chatbot_response = response, timestamp = datetime.now())
+        session.add(conversation)
+        session.commit()
+
+
+        # displaying the response
+        st.write(f"You: {user_input}")
+        st.write(f"Murphy:{response}")
+  
+# update the conversation history 
+
+        st.session_state.conversation_history.append(f"User: {user_input}")
+        st.session_state.conversation_history.append(f"Chatbot: {response}")
+
+    except Exception as e:
+        st.write(f"An error occurred: {e}")
+        session.rollback()
+    finally:
+        session.close()
+
+
+# dispaly the conversation from history 
+session = Session()
+try:
+    conversations = session.query(Conversation).all()
+    if conversations:
+        st.subheader("Conversations History")
+        for conv in conversations:
+            st.write(f"You: {conv.user_input}")
+            st.write(f"Murphy: {conv.chatbot_response}")
+except Exception as e:
+    st.write(f"Error retrieving conversations: {e}")
+finally:
     session.close()