Update app.py

app.py

@@ -1,84 +1,24 @@
-# import os
-# import gradio as gr
-# from langchain.chat_models import ChatOpenAI
-# from langchain import LLMChain, PromptTemplate
-# from langchain.memory import ConversationBufferMemory
 import os
+import openai
 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
 
-OPENAI_API_KEY=os.getenv('OPENAI_API_KEY')
+# Set OpenAI API Key
+OPENAI_API_KEY = os.getenv('OPENAI_API_KEY')
+openai.api_key = OPENAI_API_KEY
 
-template = """You are a helpful assistant to answer all user queries.
-{chat_history}
-User: {user_message}
-Chatbot:"""
+def get_text_response(user_message, history):
+    # Call OpenAI GPT model
+    response = openai.ChatCompletion.create(
+        model="gpt-3.5-turbo",
+        messages=[
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": user_message},
+        ]
+    )
+    return response['choices'][0]['message']['content']
 
-prompt = PromptTemplate(
-    input_variables=["chat_history", "user_message"], template=template
-)
-
-memory = ConversationBufferMemory(memory_key="chat_history")
-
-llm_chain = LLMChain(
-    llm=ChatOpenAI(temperature='0.5', model_name="gpt-3.5-turbo"),
-    prompt=prompt,
-    verbose=True,
-    memory=memory,
-)
-
-def get_text_response(user_message,history):
-    response = llm_chain.predict(user_message = user_message)
-    return response
-
-demo = gr.ChatInterface(get_text_response)
+# Create a Gradio chat interface
+demo = gr.Interface(fn=get_text_response, inputs="text", outputs="text")
 
 if __name__ == "__main__":
-    demo.launch() #To create a public link, set `share=True` in `launch()`. To enable errors and logs, set `debug=True` in `launch()`.
-# 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
-
-# # Get API key from environment variable
-# 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()
-
+    demo.launch()