Update app.py

app.py

@@ -1,47 +1,58 @@
 import gradio as gr
-from langchain_community.llms import HuggingFacePipeline
-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"
+from langchain_community.llms.huggingface_pipeline import HuggingFacePipeline
+from langchain_core.prompts import PromptTemplate
+from langchain_core.runnables import RunnableLambda
+from langchain_community.memory import ConversationBufferMemory
+
+# Load model and tokenizer
+model_name = "microsoft/DialoGPT-medium"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForCausalLM.from_pretrained(model_name)
 
-# Create pipeline
-pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, max_length=1000, do_sample=True)
+# Create text-generation pipeline
+pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, max_length=1000, do_sample=True, truncation=True)
 
 # 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
 )
 
-memory = ConversationBufferMemory(memory_key="chat_history")
+# Memory (updated)
+memory = ConversationBufferMemory(memory_key="chat_history", return_messages=False)
 
-llm_chain = LLMChain(
-    llm=llm,
-    prompt=prompt,
-    verbose=True,
-    memory=memory,
-)
+# Create RunnableChain (recommended instead of deprecated LLMChain)
+def generate_response(inputs):
+    formatted_prompt = prompt.format(**inputs)
+    return llm.invoke(formatted_prompt)
 
+chain = RunnableLambda(generate_response)
+
+# Gradio Chat Handler
 def get_text_response(user_message, history):
-    response = llm_chain.predict(user_message=user_message)
+    chat_history = "\n".join([f"User: {msg[0]}\nChatbot: {msg[1]}" for msg in history]) if history else ""
+    inputs = {"chat_history": chat_history, "user_message": user_message}
+    response = chain.invoke(inputs)
     return response
 
+# Gradio UI
 demo = gr.ChatInterface(
-    get_text_response,
-    examples=["How are you doing?", "What are your interests?", "Which places do you like to visit?"]
+    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"
 )
 
-
 if __name__ == "__main__":
     demo.queue().launch(share=True, debug=True)