Update app.py

app.py

@@ -1,66 +1,47 @@
 import gradio as gr
+from langchain.llms import HuggingFacePipeline
+from langchain import LLMChain, PromptTemplate
+from langchain.memory import ConversationBufferMemory
 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
 
-# Load model and tokenizer
-model_name = "microsoft/DialoGPT-medium"  # You can change this to another HF model if needed
+# Load a free model from Hugging Face
+model_name = "microsoft/DialoGPT-medium"  # Or try "tiiuae/falcon-rw-1b" or "gpt2"
 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=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
-)
+# Create pipeline
+pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, max_length=1000, do_sample=True)
 
-# Wrap with LangChain LLM wrapper
+# Wrap with HuggingFacePipeline
 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
 )
 
-# Conversation memory (stores past messages)
-memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
+memory = ConversationBufferMemory(memory_key="chat_history")
 
-# LangChain LLM Chain
 llm_chain = LLMChain(
     llm=llm,
     prompt=prompt,
+    verbose=True,
     memory=memory,
-    verbose=True
 )
 
-# Chat function
 def get_text_response(user_message, history):
     response = llm_chain.predict(user_message=user_message)
     return response
 
-# Gradio UI
 demo = gr.ChatInterface(
-    fn=get_text_response,
-    examples=["How are you doing?", "What are your interests?", "Which places do you like to visit?"],
-    title="AI Chatbot",
-    description="A simple chatbot using LangChain + HuggingFace + Gradio",
-    theme="default",
-    type="chat"  # Uses newer format to avoid Gradio tuple warnings
+    get_text_response,
+    examples=["How are you doing?", "What are your interests?", "Which places do you like to visit?"]
 )
 
-# Launch
+
 if __name__ == "__main__":
-    demo.queue().launch(share=True)
+    demo.queue().launch(share=True, debug=True)