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# 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 subprocess
# # Run the Bash script that installs dependencies and runs the app
# subprocess.run(['./run.sh'])
# # Rest of your application code can go here
# import subprocess
# import os
# # Ensure the run.sh script has executable permissions
# # subprocess.run(['chmod', '+x', './run.sh'])
# # Run the Bash script that installs dependencies and runs the app
# # subprocess.run(['./run.sh'])
# import gradio as gr
# from langchain_openai import ChatOpenAI
# from langchain.prompts import PromptTemplate
# from langchain.memory import ConversationBufferMemory
# from langchain.chains import RunnableSequence
# # 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 (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):
# 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()
# import os
# import gradio as gr
# from langchain_openai import ChatOpenAI
# from langchain.prompts import PromptTemplate
# from langchain.memory import ConversationBufferMemory
# from langchain.chains import LLMChain
# # 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 (language model) and chain
# llm = ChatOpenAI(temperature=0.5, model="gpt-3.5-turbo")
# llm_chain = LLMChain(
# llm=llm,
# 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_openai import ChatOpenAI
# from langchain.prompts import PromptTemplate
# from langchain.memory import ConversationBufferMemory
# from langchain.schema import AIMessage, HumanMessage
# from langchain.chains import RunnableSequence
# # 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 (following migration guide)
# memory = ConversationBufferMemory(return_messages=True) # Use return_messages=True for updated usage
# # Define the LLM (language model)
# llm = ChatOpenAI(temperature=0.5, model="gpt-3.5-turbo")
# # Create the RunnableSequence instead of LLMChain
# llm_sequence = prompt | llm # This pipelines the prompt into the language model
# # Function to get chatbot response
# def get_text_response(user_message, history):
# # Prepare the conversation history
# chat_history = [HumanMessage(content=user_message)]
# # Pass the prompt and history to the language model sequence
# response = llm_sequence.invoke({"chat_history": history, "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_openai import ChatOpenAI
# from langchain.prompts import PromptTemplate
# from langchain.memory import ConversationBufferMemory
# from langchain.schema import AIMessage, HumanMessage
# from langchain import Runnable # Using Runnable instead of RunnableSequence
# # 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 (following migration guide)
# memory = ConversationBufferMemory(return_messages=True) # Use return_messages=True for updated usage
# # Define the LLM (language model)
# llm = ChatOpenAI(temperature=0.5, model="gpt-3.5-turbo")
# # Create the Runnable instead of RunnableSequence
# llm_runnable = Runnable(lambda inputs: prompt.format(**inputs)) | llm
# # Function to get chatbot response
# def get_text_response(user_message, history):
# # Prepare the conversation history
# chat_history = [HumanMessage(content=user_message)]
# # Pass the prompt and history to the language model sequence
# response = llm_runnable.invoke({"chat_history": history, "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 subprocess
# import gradio as gr
# # Install necessary packages
# subprocess.check_call(["pip", "install", "-U", "langchain-openai", "gradio", "langchain-community"])
# from langchain_openai 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):
# # Prepare the conversation history
# chat_history = history + [f"User: {user_message}"]
# response = llm_chain.predict(user_message=user_message, chat_history=chat_history)
# 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 subprocess
# import gradio as gr
# # Install necessary packages
# subprocess.check_call(["pip", "install", "-U", "langchain-openai", "gradio", "langchain-community"])
# from langchain_openai import ChatOpenAI
# from langchain.prompts import PromptTemplate
# from langchain.memory import ConversationBufferMemory
# from langchain.chains import Runnable, RunnableSequence
# # 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 runnable sequence
# chatbot_runnable = RunnableSequence(prompt | ChatOpenAI(temperature=0.5, model="gpt-3.5-turbo"))
# # Function to get chatbot response
# def get_text_response(user_message, history=None):
# # Ensure history is a list
# if history is None:
# history = []
# # Prepare the conversation history
# chat_history = history + [f"User: {user_message}"]
# response = chatbot_runnable.invoke({"chat_history": "\n".join(chat_history), "user_message": user_message})
# return response
# # Create a Gradio chat interface
# demo = gr.Interface(fn=get_text_response, inputs=["text", "state"], outputs="text")
# if __name__ == "__main__":
# demo.launch()
# import os
# import subprocess
# import gradio as gr
# # Install necessary packages
# subprocess.check_call(["pip", "install", "-U", "langchain-openai", "gradio", "langchain-community"])
# from langchain_openai import ChatOpenAI
# from langchain.prompts import PromptTemplate
# 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")
# # Function to get chatbot response
# def get_text_response(user_message, history=None):
# # Ensure history is a list
# if history is None:
# history = []
# # Prepare the conversation history
# chat_history = history + [f"User: {user_message}"]
# llm = ChatOpenAI(temperature=0.5, model="gpt-3.5-turbo")
# response = llm({"chat_history": "\n".join(chat_history), "user_message": user_message})
# return response['choices'][0]['message']['content']
# # Create a Gradio chat interface
# demo = gr.Interface(fn=get_text_response, inputs=["text", "state"], outputs="text")
# if __name__ == "__main__":
# demo.launch()
# import os
# import subprocess
# import gradio as gr
# # Install necessary packages
# subprocess.check_call(["pip", "install", "-U", "langchain-openai", "gradio", "langchain-community"])
# from langchain_openai import ChatOpenAI
# from langchain.prompts import PromptTemplate
# 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")
# # Function to get chatbot response
# def get_text_response(user_message, history=None):
# # Ensure history is a list
# if history is None:
# history = []
# # Prepare the conversation history
# chat_history = history + [f"User: {user_message}"]
# llm = ChatOpenAI(temperature=0.5, model="gpt-3.5-turbo")
# response = llm({"chat_history": "\n".join(chat_history), "user_message": user_message})
# # Return the response and updated history
# return response['choices'][0]['message']['content'], chat_history
# # Create a Gradio chat interface
# demo = gr.Interface(
# fn=get_text_response,
# inputs=["text", "state"],
# outputs=["text", "state"],
# )
# if __name__ == "__main__":
# demo.launch()
# import os
# import subprocess
# import gradio as gr
# # Install necessary packages
# subprocess.check_call(["pip", "install", "-U", "langchain-openai", "gradio", "langchain-community"])
# from langchain_openai import ChatOpenAI
# from langchain.prompts import PromptTemplate
# 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")
# # Function to get chatbot response
# def get_text_response(user_message, history=None):
# # Ensure history is a list
# if history is None:
# history = []
# # Prepare the conversation history
# chat_history = history + [f"User: {user_message}"]
# # Create the full prompt string
# full_prompt = prompt.format(chat_history="\n".join(chat_history), user_message=user_message)
# llm = ChatOpenAI(temperature=0.5, model="gpt-3.5-turbo")
# # Use the invoke method instead of __call__
# response = llm.invoke(full_prompt)
# # Return the response and updated history
# return response['choices'][0]['message']['content'], chat_history
# # Create a Gradio chat interface
# demo = gr.Interface(
# fn=get_text_response,
# inputs=["text", "state"],
# outputs=["text", "state"],
# )
# if __name__ == "__main__":
# demo.launch()
import os
import gradio as gr
from langchain_openai import ChatOpenAI
from langchain.prompts import PromptTemplate
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")
# Function to get chatbot response
def get_text_response(user_message, history=None):
# Ensure history is a list
if history is None:
history = []
# Prepare the conversation history
chat_history = history + [f"User: {user_message}"]
# Create the full prompt string
full_prompt = prompt.format(chat_history="\n".join(chat_history), user_message=user_message)
llm = ChatOpenAI(temperature=0.5, model="gpt-3.5-turbo")
# Use the invoke method instead of __call__
response = llm.invoke(full_prompt)
# Return the response and updated history
return response['choices'][0]['message']['content'], chat_history
# Create a Gradio chat interface using ChatInterface
with gr.Blocks() as demo:
chatbot = gr.ChatInterface(
get_text_response, # Function to get responses
memory=ConversationBufferMemory() # Gradio stateful chat memory
)
if __name__ == "__main__":
demo.launch()
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