Update app.py

app.py

@@ -1,39 +1,43 @@
+import streamlit as st
 from langchain import LLMChain
 from langchain.chat_models import ChatOpenAI
 from langchain.prompts import ChatPromptTemplate
-from langchain.output_parsers import StrOutputParser
-import streamlit as st
-import os
 from dotenv import load_dotenv
+import os
 
 # Load environment variables
 load_dotenv()
 
-# Set environment variables
+# Set LangChain tracing (optional)
 os.environ["LANGCHAIN_TRACING_V2"] = "true"
 os.environ["LANGCHAIN_API_KEY"] = os.getenv("LANGCHAIN_API_KEY")
 
-# Initialize the LLM (e.g., using OpenAI or Hugging Face)
-llm = ChatOpenAI(model_name="gpt-3.5-turbo", openai_api_key=os.getenv("OPENAI_API_KEY"))
+# Initialize the Hugging Face LLaMA 2 model via LangChain
+llm = ChatOpenAI(
+    model_name="meta-llama/Llama-2-7b-chat-hf",
+    temperature=0.7,
+    max_tokens=512,
+    openai_api_key=os.getenv("OPENAI_API_KEY")  # If using OpenAI; otherwise, remove
+)
 
-# Prompt Template
+# Define the prompt template
 prompt = ChatPromptTemplate.from_messages(
     [
-        ("system", "You are a helpful assistant. Please respond to the user queries."),
+        ("system", "You are a helpful assistant."),
         ("user", "Question: {question}")
     ]
 )
 
-# Create LLM Chain
+# Create the LLM Chain
 chain = LLMChain(llm=llm, prompt=prompt, output_key="response")
 
-# Streamlit app setup
-st.title('Langchain Demo with Hugging Face API')
+# Streamlit App Interface
+st.title('LangChain Demo with LLaMA 2 on Hugging Face')
 
 # User input
-input_text = st.text_input("Search the topic you want")
+input_text = st.text_input("Enter your question:")
 
-# Display result when user inputs text
+# Display the response
 if input_text:
     try:
         response = chain.run({"question": input_text})