Update app.py

app.py

@@ -1,41 +1,24 @@
-import subprocess
 
-# Instalar un paquete utilizando pip desde Python
-subprocess.check_call(["pip", "install", "langchain_community","langchain"])
 
-import streamlit as st 
-from langchain.chains import LLMChain
-from langchain.chains import ConversationChain
-#importacipones adicionales 
+# Import necessary libraries
 import streamlit as st
 from langchain.chains import ConversationChain
-from langchain.memory import ConversationBufferMemory
-from langchain.chains import ConversationChain
-from langchain.prompts import PromptTemplate
-
 from langchain.chains.conversation.memory import ConversationEntityMemory
 from langchain.chains.conversation.prompt import ENTITY_MEMORY_CONVERSATION_TEMPLATE
-from langchain_community.llms import HuggingFaceEndpoint
 import os
-from langchain.memory import ConversationBufferWindowMemory
-
-
-st.set_page_config(page_title= "bot", layout="wide")
+from getpass import getpass
+from langchain import HuggingFaceHub
+from langchain_community.llms import HuggingFaceEndpoint
 
-#Interfaz
-st.title("Maqueta")
 
-#template
+#token de hugging face 
 
-#llm
+os.environ['HUGGINGFACEHUB_API_TOKEN'] = "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"
 
-llm = HuggingFaceEndpoint(repo_id='tiiuae/falcon-7b',
-                          model_kwargs = {"max_length":64},
-                          temperature=0.5, 
-                          huggingfacehub_api_token = os.environ["HUGGINGFACEHUB_API_TOKEN"])
-
-#memory
 
+# Set Streamlit page configuration
+st.set_page_config(page_title='🧠MemoryBot🤖', layout='wide')
+# Initialize session states. Un session state es como un diccionario
 if "generated" not in st.session_state:
     st.session_state["generated"] = []
 if "past" not in st.session_state:
@@ -45,9 +28,7 @@ if "input" not in st.session_state:
 if "stored_session" not in st.session_state:
     st.session_state["stored_session"] = []
 
-
-
-
+# Define function to get user input
 def get_text():
     """
     Get the user input text.
@@ -60,24 +41,84 @@ def get_text():
                             label_visibility='hidden')
     return input_text
 
-user_input = get_text()
+# #parte para hacer un chat nuevo 
+def new_chat():
+    """
+    Clears session state and starts a new chat.
+    """
+    save = []
+    for i in range(len(st.session_state['generated'])-1, -1, -1):
+        save.append("User:" + st.session_state["past"][i])
+        save.append("Bot:" + st.session_state["generated"][i])        
+    st.session_state["stored_session"].append(save)
+    st.session_state["generated"] = []
+    st.session_state["past"] = []
+    st.session_state["input"] = ""
+    st.session_state.entity_memory.entity_store = {}
+    st.session_state.entity_memory.buffer.clear()
+
+# Add a button to start a new chat
+st.sidebar.button("New Chat", on_click = new_chat, type='primary')
 
 
-#op1
-memory = ConversationBufferMemory()
-Conversation = ConversationChain(llm = llm,
-                                 memory = memory)
 
-#opc2:BUFFER WINDOW MEMORY
-memory2 = ConversationBufferWindowMemory(k=3) #k hace referencia al numero de conversaiones que queremos almacenar 
-conversation_buff2 = ConversationChain(llm = llm,
-                                       memory = memory2)
 
+# Move K outside of the sidebar expander
+K = st.sidebar.number_input(' (#)Summary of prompts to consider', min_value=3, max_value=1000)
+
+# Set up the Streamlit app layout
+st.title("Personalized chatbot")
+
+
+
+# Create an OpenAI instance
+llm = HuggingFaceEndpoint(repo_id='meta-llama/Llama-2-7b-chat-hf', 
+                          max_length=128, 
+                          temperature=0.5, 
+                          token="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")
+
+
+
+
+
+
+
+# Create a ConversationEntityMemory object if not already created
+if 'entity_memory' not in st.session_state:
+    st.session_state.entity_memory = ConversationEntityMemory(llm=llm, k=K )
+    
+    # Create the ConversationChain object with the specified configuration
+Conversation = ConversationChain(
+        llm=llm, 
+        prompt=ENTITY_MEMORY_CONVERSATION_TEMPLATE,
+        memory=st.session_state.entity_memory
+    )  
+
+
+# Get the user input
+user_input = get_text()
+
+# Generate the output using the ConversationChain object and the user input, and add the input/output to the session
+if user_input:
+    output = Conversation.run(input=user_input)  
+    st.session_state.past.append(user_input)  
+    st.session_state.generated.append(output)  
+
+
+# Display the conversation history using an expander, and allow the user to download it
+with st.expander("Conversation", expanded=True):
+    for i in range(len(st.session_state['generated'])-1, -1, -1):
+        st.info(st.session_state["past"][i],icon="🧐")
+        st.success(st.session_state["generated"][i], icon="🤖")
 
 
-submit = st.button("Generate")
 
-while submit:
-    output = Conversation.invoke(input=user_input)  
-  
+# Display stored conversation sessions in the sidebar
+for i, sublist in enumerate(st.session_state.stored_session):
+        with st.sidebar.expander(label= f"Conversation-Session:{i}"):
+            st.write(sublist)
 
+# Allow the user to clear all stored conversation sessions
+if st.session_state.stored_session:   
+    if st.sidebar.checkbox("Clear-all"):
+        del st.session_state.stored_session