Upload app.py

app.py

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+from langchain import HuggingFaceHub
+import os
+from dotenv import load_dotenv
+
+load_dotenv()  # take environment variables from .env.
+
+import streamlit as st
+
+## Function to load OpenAI model and get responses
+def get_ai_response(context, question):
+    llm = HuggingFaceHub(
+        repo_id='EleutherAI/gpt-neo-2.7B',
+        huggingfacehub_api_token=os.getenv('HUGGINGFACEHUB_API_TOKEN'),
+        model_kwargs={
+            'temperature': 0.6,
+            'max_length': 1000
+        }
+    )
+    # input_data = {"context": context, "question": question}
+    # response = llm(input_data)
+    prompt = f"Context: {context}\nQuestion: {question}\nAnswer:"
+    response = llm(prompt)
+    return response
+
+## Initialize our Streamlit app
+st.set_page_config(page_title="Q&A Demo")
+
+st.header("Langchain Application")
+
+context = st.text_area("Context: ", key="context")
+question = st.text_input("Question: ", key="question")
+submit = st.button("Ask the question")
+
+## If ask button is clicked
+if submit:
+    response = get_ai_response(context, question)
+    st.subheader("The Response is")
+    st.write(response)