Spaces:
Sleeping
Sleeping
File size: 3,961 Bytes
0318caf 27992f6 0b60445 27992f6 cac8f0f 27992f6 d35c879 27992f6 d5a8216 27992f6 417a23b 27992f6 cac8f0f 744d8dc 27992f6 cac8f0f 27992f6 cac8f0f 27992f6 cac8f0f 27992f6 79a62a7 cac8f0f d35c879 b307974 0318caf 4b46c96 417a23b 0d53d23 dda8f2a d141979 25606a9 8f6ec4b 744d8dc 0b60445 378a7ec 27992f6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 |
# 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.schema import AIMessage, HumanMessage
# # Set OpenAI API Key
# os.environ["OPENAI_API_KEY"] = "sk-3_mJiR5z9Q3XN-D33cgrAIYGffmMvHfu5Je1U0CW1ZT3BlbkFJA2vfSvDqZAVUyHo2JIcU91XPiAq424OSS8ci29tWMA" # Replace with your key
# # Initialize the ChatOpenAI model
# llm = ChatOpenAI(temperature=1.0, model="gpt-3.5-turbo-0613")
# # Function to predict response
# def get_text_response(message, history=None):
# # Ensure history is a list
# if history is None:
# history = []
# # Convert the Gradio history format to LangChain message format
# history_langchain_format = []
# for human, ai in history:
# history_langchain_format.append(HumanMessage(content=human))
# history_langchain_format.append(AIMessage(content=ai))
# # Add the new user message to the history
# history_langchain_format.append(HumanMessage(content=message))
# # Get the model's response
# gpt_response = llm(history_langchain_format)
# # Append AI response to history
# history.append((message, gpt_response.content))
# # Return the response and updated history
# return gpt_response.content, history
# # Create a Gradio chat interface
# demo = gr.ChatInterface(
# fn=get_text_response,
# inputs=["text", "state"],
# outputs=["text", "state"]
# )
# if __name__ == "__main__":
# demo.launch()
import os # Import the os module
import time
import gradio as gr
from langchain_community.chat_models import ChatOpenAI # Updated import based on deprecation warning
from langchain.schema import AIMessage, HumanMessage
import openai
# Set your OpenAI API key
os.environ["OPENAI_API_KEY"] = "sk-3_mJiR5z9Q3XN-D33cgrAIYGffmMvHfu5Je1U0CW1ZT3BlbkFJA2vfSvDqZAVUyHo2JIcU91XPiAq424OSS8ci29tWMA" # Replace with your OpenAI key
# Initialize ChatOpenAI
llm = ChatOpenAI(temperature=1.0, model='gpt-3.5-turbo-0613')
def predict(message, history):
# Reformat history for LangChain
history_langchain_format = []
for human, ai in history:
history_langchain_format.append(HumanMessage(content=human))
history_langchain_format.append(AIMessage(content=ai))
# Add latest human message
history_langchain_format.append(HumanMessage(content=message))
# Get response from the model
gpt_response = llm(history_langchain_format)
# Return response
return gpt_response.content
# Using ChatInterface to create a chat-style UI
demo = gr.Interface(fn=predict, type="messages")
if __name__ == "__main__":
demo.launch()
|