Create app.py

app.py

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+from langchain_community.llms import Ollama
+import streamlit as st 
+from langchain.chains import LLMChain
+from langchain.chains import ConversationChain
+#importacipones adicionales 
+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.llms import Ollama
+
+
+st.set_page_config(page_title= "bot", layout="wide")
+
+#Interfaz
+st.title("Maqueta")
+
+
+
+#template
+
+
+
+
+#llm
+
+llm = HuggingFaceEndpoint(repo_id='mistralai/Mistral-7B-v0.1', 
+                          max_length=128, 
+                          temperature=0.5)
+
+#memory
+
+
+
+conversation_buf = ConversationChain(llm = llm,
+                      memory = ConversationBufferMemory(),
+                      verbose = True)
+
+
+
+if "generated" not in st.session_state:
+    st.session_state["generated"] = []
+if "past" not in st.session_state:
+    st.session_state["past"] = []
+if "input" not in st.session_state:
+    st.session_state["input"] = ""
+if "stored_session" not in st.session_state:
+    st.session_state["stored_session"] = []
+
+
+def get_text():
+    """
+    Get the user input text.
+
+    Returns:
+        (str): The text entered by the user
+    """
+    input_text = st.text_input("You: ", st.session_state["input"], key="input",
+                            placeholder="Your AI assistant here! Ask me anything ...", 
+                            label_visibility='hidden')
+    return input_text
+
+user_input = get_text()
+
+if 'entity memory' not in st.session_state:
+    st.session_state.entity_memory = ConversationEntityMemory(llm = llm,k=10)
+
+Conversation = ConversationChain(llm = llm, 
+                                 prompt= ENTITY_MEMORY_CONVERSATION_TEMPLATE,
+                                 memory = st.session_state.entity_memory)
+
+
+while True:
+    
+
+    output = Conversation.run(input=user_input)  
+    st.session_state.past.append(user_input)  
+    st.session_state.generated.append(output)  
+