Upload SimpleChatbot.py

SimpleChatbot.py

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+# Q&A Chatbot
+from langchain_community.llms import HuggingFaceEndpoint
+from langchain.chains import LLMChain
+from langchain.prompts import PromptTemplate
+
+# from langchain.llms import OpenAI
+# uncomment above line when I have credit in OpenAI
+from dotenv import load_dotenv
+load_dotenv() # take environment variables from .env
+
+import streamlit as st
+import os
+
+## Function to load AI model and get responses. Here I can incorporate prompt template also
+
+def get_model_response(question):
+    llm = HuggingFaceEndpoint(
+        repo_id="mistralai/Mistral-7B-Instruct-v0.2", max_length=128, temperature=0.5, token=os.getenv("HUGGINGFACEHUB_API_TOKEN"))
+    
+    # llm = OpenAI(openai_api_key=os.getenv("OPENAI_API_KEY"), model_name="text-davinci-003", temperature=0.6, max_tokens=64)
+    # uncomment above line and use it instead of HuggingfaceEndpoint when I have credit in OpenAI.
+    template = """Question: {question}
+    Answer:"""
+    prompt = PromptTemplate.from_template(template)
+    llm_chain = LLMChain(prompt=prompt, llm=llm)
+    response = llm_chain.invoke({"question": question})
+    return response
+
+
+## Initialize our StreamLit app
+st.set_page_config(page_title="Simple Chatbot")
+
+st.header("Langchain Application - Simple Chatbot")
+
+input = st.text_input("Input: ", key="input")
+response = get_model_response(input)
+
+submit = st.button("Ask the question")
+
+## If ask button is clicked
+if submit:
+    st.subheader("The response is: ")
+    st.write(response)