Create app.py

app.py

@@ -0,0 +1,36 @@
+from langchain_community.embeddings import HuggingFaceBgeEmbeddings
+from langchain_community.llms import HuggingFaceEndpoint
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_community.llms import HuggingFaceHub
+from langchain_core.output_parsers import StrOutputParser
+from langchain import PromptTemplate, LLMChain
+from langchain_groq import ChatGroq
+import streamlit as st
+import os
+def get_response(input_text):
+    groq_api_key="GROK-API-KEY"
+    llm=ChatGroq(groq_api_key=groq_api_key,
+         model_name="mixtral-8x7b-32768")
+    prompt=ChatPromptTemplate.from_messages(
+    [
+        ("system","You are the equivalent of a therapist. Please be empathetic and kind towards the user think of them as a person who has no one to talk to.Ask them what they need. Or what they want or if they need someone to listen to."),
+        ("user","Question:{question}")
+    ]
+    )
+    #prompt = PromptTemplate(template=prompt, input_variables=["question"])
+    #llm_chain = LLMChain(prompt=prompt, llm=llm)
+    output_parser=StrOutputParser()
+    chain=prompt|llm|output_parser
+    response=chain.invoke({"question":input_text})
+    return response
+def main():
+    st.title("Therapy Bot")
+    input_text=st.text_input("How are you Today ?")
+    if st.button("Say"):
+        if input_text.strip() !="":
+            response=get_response(input_text)
+            st.write(response)
+        else:
+            st.warning("Please enter a question.")
+if __name__=="__main__":
+    main()