Update app.py

app.py

@@ -1,9 +1,54 @@
+# import os
+# import gradio as gr
+# from langchain.chat_models import ChatOpenAI
+# from langchain.prompts import PromptTemplate
+# from langchain.chains import LLMChain
+# from langchain.memory import ConversationBufferMemory
+
+# # Set OpenAI API Key
+# OPENAI_API_KEY = os.getenv('OPENAI_API_KEY')
+
+# # Define the template for the chatbot's response
+# template = """You are a helpful assistant to answer all user queries.
+# {chat_history}
+# User: {user_message}
+# Chatbot:"""
+
+# # Define the prompt template
+# prompt = PromptTemplate(
+#     input_variables=["chat_history", "user_message"], 
+#     template=template
+# )
+
+# # Initialize conversation memory
+# memory = ConversationBufferMemory(memory_key="chat_history")
+
+# # Define the LLM chain with the ChatOpenAI model and conversation memory
+# llm_chain = LLMChain(
+#     llm=ChatOpenAI(temperature=0.5, model="gpt-3.5-turbo"),  # Use 'model' instead of 'model_name'
+#     prompt=prompt,
+#     verbose=True,
+#     memory=memory,
+# )
+
+# # Function to get chatbot response
+# def get_text_response(user_message, history):
+#     response = llm_chain.predict(user_message=user_message)
+#     return response
+
+# # Create a Gradio chat interface
+# demo = gr.Interface(fn=get_text_response, inputs="text", outputs="text")
+
+# if __name__ == "__main__":
+#     demo.launch()
+
 import os
 import gradio as gr
-from langchain.chat_models import ChatOpenAI
+from langchain_openai import ChatOpenAI
 from langchain.prompts import PromptTemplate
-from langchain.chains import LLMChain
+from langchain.schema import BaseMemory
 from langchain.memory import ConversationBufferMemory
+from langchain.chains import RunnableSequence
 
 # Set OpenAI API Key
 OPENAI_API_KEY = os.getenv('OPENAI_API_KEY')
@@ -23,21 +68,21 @@ prompt = PromptTemplate(
 # Initialize conversation memory
 memory = ConversationBufferMemory(memory_key="chat_history")
 
-# Define the LLM chain with the ChatOpenAI model and conversation memory
-llm_chain = LLMChain(
-    llm=ChatOpenAI(temperature=0.5, model="gpt-3.5-turbo"),  # Use 'model' instead of 'model_name'
-    prompt=prompt,
-    verbose=True,
-    memory=memory,
-)
+# Define the LLM (language model)
+llm = ChatOpenAI(temperature=0.5, model="gpt-3.5-turbo")
+
+# Define the chain using RunnableSequence (replace LLMChain)
+llm_chain = prompt | llm  # Chaining the prompt and the LLM
 
 # Function to get chatbot response
 def get_text_response(user_message, history):
-    response = llm_chain.predict(user_message=user_message)
-    return response
+    inputs = {"chat_history": history, "user_message": user_message}
+    response = llm_chain(inputs)
+    return response['text']
 
 # Create a Gradio chat interface
 demo = gr.Interface(fn=get_text_response, inputs="text", outputs="text")
 
 if __name__ == "__main__":
     demo.launch()
+