Update app.py

app.py

@@ -4,44 +4,48 @@ from langchain import LLMChain, PromptTemplate
 from langchain.memory import ConversationBufferMemory
 from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
 
-# Load a free model from Hugging Face
-model_name = "microsoft/DialoGPT-medium"  # Or try "tiiuae/falcon-rw-1b" or "gpt2"
+# Load model and tokenizer
+model_name = "microsoft/DialoGPT-medium"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForCausalLM.from_pretrained(model_name)
 
-# Create pipeline
+# Create HF pipeline
 pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, max_length=1000, do_sample=True)
 
-# Wrap with HuggingFacePipeline
+# Wrap with HuggingFacePipeline for LangChain
 llm = HuggingFacePipeline(pipeline=pipe)
 
+# Prompt Template
 template = """You are a helpful assistant to answer user queries.
 {chat_history}
 User: {user_message}
 Chatbot:"""
 
 prompt = PromptTemplate(
-    input_variables=["chat_history", "user_message"], template=template
+    input_variables=["chat_history", "user_message"],
+    template=template
 )
 
+# Memory
 memory = ConversationBufferMemory(memory_key="chat_history")
 
+# LLM Chain
 llm_chain = LLMChain(
     llm=llm,
     prompt=prompt,
-    verbose=True,
     memory=memory,
 )
 
+# Response function
 def get_text_response(user_message, history):
     response = llm_chain.predict(user_message=user_message)
     return response
 
+# Gradio Chat Interface
 demo = gr.ChatInterface(
     get_text_response,
     examples=["How are you doing?", "What are your interests?", "Which places do you like to visit?"]
 )
 
-
-if __name__ == "__main__":
-    demo.queue().launch(share=True, debug=True)
+# Launch the app (no share=True needed for Spaces)
+demo.launch()