Update app.py

app.py

@@ -1,40 +1,63 @@
 import streamlit as st
-import time
 from queue import Queue
+from langchain import HuggingFaceHub, PromptTemplate, LLMChain
 
+# Set the title of the Streamlit app
 st.title("Falcon QA Bot")
 
+# Get the Hugging Face Hub API token from Streamlit secrets
 huggingfacehub_api_token = st.secrets["hf_token"]
 
-from langchain import HuggingFaceHub, PromptTemplate, LLMChain
-
+# Set the repository ID for the Falcon model
 repo_id = "tiiuae/falcon-7b-instruct"
-llm = HuggingFaceHub(huggingfacehub_api_token=huggingfacehub_api_token, 
-                     repo_id=repo_id, 
-                     model_kwargs={"temperature":0.2, "max_new_tokens":2000})
 
+# Initialize the Hugging Face Hub and LLMChain
+llm = HuggingFaceHub(
+    huggingfacehub_api_token=huggingfacehub_api_token,
+    repo_id=repo_id,
+    model_kwargs={"temperature": 0.2, "max_new_tokens": 2000}
+)
+
+# Define the template for the assistant's response
 template = """
 You are an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.
 
 {question}
-
 """
 
+# Create a queue to store user questions
 queue = Queue()
 
 def chat(query):
+    """
+    Generates a response to the user's question using the LLMChain model.
+
+    :param query: User's question.
+    :type query: str
+    :return: Response to the user's question.
+    :rtype: str
+    """
+    # Create a prompt template with the question variable
     prompt = PromptTemplate(template=template, input_variables=["question"])
-    llm_chain = LLMChain(prompt=prompt,verbose=True,llm=llm)
 
+    # Create an LLMChain instance with the prompt and the Falcon model
+    llm_chain = LLMChain(prompt=prompt, verbose=True, llm=llm)
+
+    # Generate a response to the user's question
     result = llm_chain.predict(question=query)
 
     return result
 
 def main():
-    input = st.text_input("What do you want to ask about", placeholder="Input your question here")
-    if input:
+    """
+    Main function for the Streamlit app.
+    """
+    # Get the user's question from the input text box
+    user_question = st.text_input("What do you want to ask about", placeholder="Input your question here")
+
+    if user_question:
         # Add the user's question to the queue
-        queue.put(input)
+        queue.put(user_question)
 
         # Check if there are any waiting users
         if not queue.empty():
@@ -42,10 +65,10 @@ def main():
             query = queue.get()
 
             # Generate a response to the user's question
-            result = chat(query)
+            response = chat(query)
 
             # Display the response to the user
-            st.write(result,unsafe_allow_html=True)
+            st.write(response, unsafe_allow_html=True)
 
 if __name__ == '__main__':
-    main()
+    main()