Delete day2

day2/chatbot.py

@@ -1,16 +0,0 @@
-cat > day2/chatbot.py << 'PY'
-from dotenv import load_dotenv
-load_dotenv()
-
-import gradio as gr
-from langchain_groq import ChatGroq
-
-def chat(message, history):
-    llm = ChatGroq(model="llama3-8b-8192")
-    resp = llm.invoke(message)
-    return resp.content
-
-demo = gr.ChatInterface(chat)
-if __name__ == "__main__":
-    demo.launch()
-PY

day2/first_chain.py

@@ -1,39 +0,0 @@
-from dotenv import load_dotenv
-import os
-from langchain_groq import ChatGroq
-from langchain.prompts import PromptTemplate
-from langchain.chains import LLMChain
-
-load_dotenv()
-
-# Step 1: Set up the LLM (Groq with LLaMA 3.1)
-llm = ChatGroq(model="llama3-8b-8192")
-
-# Step 2: Define prompt template with Azerbaijan context
-template = """
-You are an expert assistant with deep knowledge about Azerbaijan.
-
-Here is some context about Azerbaijan:
-Azerbaijan is a country located at the crossroads of Eastern Europe and Western Asia. It is known for its rich culture, history, oil resources, and modern capital Baku.
-
-Now, answer the following question clearly and concisely:   
-{question}
-"""
-
-prompt = PromptTemplate.from_template(template) 
-
-# Step 3: Create the LangChain LLMChain     
-chain = LLMChain(llm=llm, prompt=prompt)
-
-# Step 4: Run the chain with a user question
-if __name__ == "__main__":
-    user_question = input("Enter your question: ")
-    answer = chain.run(user_question)
-    print("\nAnswer:\n", answer)
-# Step 5: Test the chain with a sample question
-    # Example: "What is the capital of Azerbaijan?"
-    # This will prompt the user to enter a question and provide an answer based on the context
-    # Note: Ensure you have the necessary environment set up to run this code, including the
-    # LangChain and Groq libraries installed and configured.
-    # You can run this script in an environment where the Groq model is accessible.
-    # Make sure to handle any exceptions or errors that may arise during execution.