Update app.py

app.py

@@ -3,35 +3,6 @@
 # from langchain.chat_models import ChatOpenAI
 # from langchain import LLMChain, PromptTemplate
 # from langchain.memory import ConversationBufferMemory
-
-# OPENAI_API_KEY=os.getenv('OPENAI_API_KEY')
-
-# template = """You are a helpful assistant to answer all user queries.
-# {chat_history}
-# User: {user_message}
-# Chatbot:"""
-
-# 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)
-
-# 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
@@ -39,40 +10,75 @@ 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')
+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
+    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'
+    llm=ChatOpenAI(temperature='0.5', model_name="gpt-3.5-turbo"),
     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)
+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")
+demo = gr.ChatInterface(get_text_response)
 
 if __name__ == "__main__":
-    demo.launch()
+    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()