Upload 3 files

.env

@@ -0,0 +1,2 @@
+# OPENAI_API_KEY=""
+HUGGINGFACEHUB_API_TOKEN=""

app.py

@@ -1,31 +1,39 @@
 import streamlit as st
+from langchain.llms import HuggingFaceHub
+from langchain import PromptTemplate, LLMChain
 
-# from langchain.llms import OpenAI
-from langchain.llms import HuggingFace
-
-## Function to return the response
+## Function to return the response using the HuggingFace Model
 def load_answer(question):
-    # llm = OpenAI(model_name="gpt-3.5-turbo")
-    llm = HuggingFace(model_name="google/flan-t5-large")
-    answer = llm(question)
+    # Initialize the HuggingFaceHub LLM
+    llm = HuggingFaceHub(
+        repo_id="google/flan-t5-large",
+        # huggingfacehub_api_token="",
+        model_kwargs={"temperature": 0}
+    )
+
+    # Create a prompt template for the question
+    template = PromptTemplate(input_variables=["question"], template="{question}")
+    llm_chain = LLMChain(prompt=template, llm=llm)
+
+    # Generate the answer using the LLM chain
+    answer = llm_chain.run(question)
     return answer
 
-## App UI starts here
-st.set_page_config(page_title="LangChain Demo", page_icon="🦜")
-st.header("🦜️🔗 LangChain Demo")
+# App UI starts here
+st.set_page_config(page_title="LangChain Demo", page_icon=":robot:")
+st.header("LangChain Demo")
 
-## Gets the user input
+# Gets the user input
 def get_text():
     input_text = st.text_input("You: ", key="input")
     return input_text
 
 user_input = get_text()
-response = load_answer(user_input)
 
-submit = st.button("Generate")
+submit = st.button('Generate')
 
-## If generate button is clicked
-if submit:
+# If generate button is clicked
+if submit and user_input:
+    response = load_answer(user_input)
     st.subheader("Answer:")
-
     st.write(response)

requirements.txt

@@ -1,3 +1,5 @@
-langchain
-Openai
-Streamlit
+langchain
+langchain-community
+# Openai
+streamlit
+huggingface_hub