Spaces:
Sleeping
Sleeping
| # 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() | |