Update app.py

app.py

@@ -1,60 +1,47 @@
 import gradio as gr
-from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
-from langchain_huggingface import HuggingFacePipeline  # Updated import
-from langchain_core.prompts import PromptTemplate
-from langchain_core.runnables import RunnableLambda
+from langchain.llms import HuggingFacePipeline
+from langchain import LLMChain, PromptTemplate
 from langchain.memory import ConversationBufferMemory
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
 
-# Load model and tokenizer
-model_name = "microsoft/DialoGPT-medium"
+# 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 HuggingFace pipeline
+# Create pipeline
 pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, max_length=1000, do_sample=True)
 
-# Wrap with updated HuggingFacePipeline from langchain_huggingface
+# Wrap with HuggingFacePipeline
 llm = HuggingFacePipeline(pipeline=pipe)
 
-# Prompt Template
-template = """You are a helpful assistant that answers user queries.
+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
 )
 
-# Updated memory object
-memory = ConversationBufferMemory(memory_key="chat_history", return_messages=False)
-
-# Response generation function
-def generate_response(inputs):
-    formatted_prompt = prompt.format(**inputs)
-    return llm.invoke(formatted_prompt)
+memory = ConversationBufferMemory(memory_key="chat_history")
 
-# Wrap as chain
-chain = RunnableLambda(generate_response)
+llm_chain = LLMChain(
+    llm=llm,
+    prompt=prompt,
+    verbose=True,
+    memory=memory,
+)
 
-# Chat handler for Gradio
-def get_text_response(message, history):
-    # Convert history to chat format
-    chat_history = "\n".join([f"User: {turn['content']}\nChatbot: {response['content']}" for turn, response in zip(history[::2], history[1::2])]) if history else ""
-    inputs = {"chat_history": chat_history, "user_message": message}
-    response = chain.invoke(inputs)
-    return {"role": "assistant", "content": response}
+def get_text_response(user_message, history):
+    response = llm_chain.predict(user_message=user_message)
+    return response
 
-# Gradio ChatInterface with updated format
 demo = gr.ChatInterface(
-    fn=get_text_response,
-    chatbot=gr.Chatbot(type="messages"),
-    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 (2025)",
-    theme="default"
+    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)