Update app.py

app.py

@@ -42,467 +42,6 @@
 # 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.chat_models import ChatOpenAI