Update app.py

app.py

@@ -1,11 +1,6 @@
 import gradio as gr
 from langchain import HuggingFaceHub, PromptTemplate, LLMChain
 from langchain.memory import ConversationBufferMemory
-import os
-
-# Get the HuggingFace API token from the environment variable
-# huggingfacehub_api_token = os.environ.get("HUGGINGFACEHUB_API_TOKEN")
-huggingfacehub_api_token = None
 
 repo_id = "tiiuae/falcon-7b-instruct"
 
@@ -17,17 +12,22 @@ Chatbot:"""
 prompt = PromptTemplate(template=template, input_variables=["chat_history","human_input"])
 
 def generate_response(question, huggingfacehub_api_token, temperature=0.6, max_new_tokens=500):
-    memory = ConversationBufferMemory(memory_key="chat_history")
-    llm = HuggingFaceHub(huggingfacehub_api_token=huggingfacehub_api_token, 
-                         repo_id=repo_id, 
-                         model_kwargs={"temperature": temperature, 
-                                       "max_new_tokens": max_new_tokens})
-    llm_chain = LLMChain(prompt=prompt, llm=llm, memory=memory)
-    return llm_chain.predict(chat_history="", human_input=question)
+    try:
+        memory = ConversationBufferMemory(memory_key="chat_history")
+        llm = HuggingFaceHub(huggingfacehub_api_token=huggingfacehub_api_token, 
+                             repo_id=repo_id, 
+                             model_kwargs={"temperature": temperature, 
+                                           "max_new_tokens": max_new_tokens})
+        llm_chain = LLMChain(prompt=prompt, llm=llm, memory=memory)
+        response = llm_chain.predict(chat_history="", human_input=question)
+    except ValueError as e:
+        response = "An error occurred while processing your request. Please try again later."
+        print(f"Error: {str(e)}")
+    return response
 
 inputs = [
     gr.inputs.Textbox(label="Question"),
-    gr.inputs.Textbox(label="HuggingFace API Token", type="password", default=huggingfacehub_api_token),
+    gr.inputs.Textbox(label="HuggingFace API Token", type="password", default=None),
     gr.inputs.Slider(minimum=0.1, maximum=2.0, default=0.6, label="Temperature"),
     gr.inputs.Slider(minimum=100, maximum=1000, default=500, label="Max New Tokens")
 ]