Update app.py

app.py

@@ -1,19 +1,27 @@
 import gradio as gr
 from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
 from langchain_community.llms import HuggingFacePipeline
+from langchain_community.memory import ConversationBufferMemory
 from langchain_core.prompts import PromptTemplate
 from langchain.chains import LLMChain
-from langchain_core.memory import ConversationBufferMemory
 
 # Load model and tokenizer
-model_name = "microsoft/DialoGPT-medium"
+model_name = "microsoft/DialoGPT-medium"  # You can change this to another HF model if needed
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForCausalLM.from_pretrained(model_name)
 
 # Create text-generation pipeline
-pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, max_length=10000, do_sample=True, truncation=True)
+pipe = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    max_length=1000,
+    do_sample=True,
+    truncation=True,  # Explicit truncation to avoid HF warnings
+    pad_token_id=tokenizer.eos_token_id  # Prevents warning for open-end generation
+)
 
-# Wrap with HuggingFacePipeline
+# Wrap with LangChain LLM wrapper
 llm = HuggingFacePipeline(pipeline=pipe)
 
 # Prompt Template
@@ -27,10 +35,10 @@ prompt = PromptTemplate(
     template=template
 )
 
-# Memory
+# Conversation memory (stores past messages)
 memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
 
-# Chain
+# LangChain LLM Chain
 llm_chain = LLMChain(
     llm=llm,
     prompt=prompt,
@@ -50,8 +58,9 @@ demo = gr.ChatInterface(
     title="AI Chatbot",
     description="A simple chatbot using LangChain + HuggingFace + Gradio",
     theme="default",
-    chatbot=gr.Chatbot(label="Assistant", show_label=True)
+    type="chat"  # Uses newer format to avoid Gradio tuple warnings
 )
 
+# Launch
 if __name__ == "__main__":
     demo.queue().launch(share=True)