app file

app.py

@@ -0,0 +1,53 @@
+from dotenv import load_dotenv
+load_dotenv()
+
+import streamlit as st
+import os
+#from langchain import HuggingFaceHub
+from langchain_community.llms import HuggingFaceEndpoint
+from langchain.chains import LLMChain
+from langchain.prompts import PromptTemplate
+
+
+
+print(os.environ["HUGGINGFACEHUB_API_TOKEN"])
+def get_llm_response(question):
+    
+    HUGGINGFACEHUB_API_TOKEN=os.environ["HUGGINGFACEHUB_API_TOKEN"]
+    repo_id = "mistralai/Mistral-7B-Instruct-v0.2"
+
+
+
+    template = """Question: {question}
+
+    Answer: """
+
+    prompt = PromptTemplate.from_template(template)
+
+
+    llm = HuggingFaceEndpoint(
+        repo_id=repo_id, max_length=128, temperature=0.5, token=HUGGINGFACEHUB_API_TOKEN
+    )
+    llm_chain = LLMChain(prompt=prompt, llm=llm)
+    answer=llm_chain.run(question)
+    print(answer)
+
+    return answer
+
+##initialize streamlit 
+st.set_page_config(page_title="Q&A Demo")
+st.header("Langchain application")
+
+question=st.text_input("input: ",key="input")
+
+response=get_llm_response(question)
+submit=st.button("Ask the question")
+
+
+#if submit is clicked
+
+if submit:
+    st.subheader("The response is")
+    st.write(response)
+
+