Update app.py

app.py

@@ -42,80 +42,47 @@
 # 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.Interface(
-#     fn=get_text_response, 
-#     inputs=["text", "state"], 
-#     outputs=["text", "state"]
-# )
-
-# if __name__ == "__main__":
-#     demo.launch()
-
-import time
+import os
 import gradio as gr
 from langchain.chat_models import ChatOpenAI
 from langchain.schema import AIMessage, HumanMessage
-import openai
 
-os.environ["OPENAI_API_KEY"] = "sk-3_mJiR5z9Q3XN-D33cgrAIYGffmMvHfu5Je1U0CW1ZT3BlbkFJA2vfSvDqZAVUyHo2JIcU91XPiAq424OSS8ci29tWMA" 
+# Set OpenAI API Key
+os.environ["OPENAI_API_KEY"] = "sk-3_mJiR5z9Q3XN-D33cgrAIYGffmMvHfu5Je1U0CW1ZT3BlbkFJA2vfSvDqZAVUyHo2JIcU91XPiAq424OSS8ci29tWMA"  # Replace with your key
 
-# Initialize ChatOpenAI
-llm = ChatOpenAI(temperature=1.0, model='gpt-3.5-turbo-0613')
+# Initialize the ChatOpenAI model
+llm = ChatOpenAI(temperature=1.0, model="gpt-3.5-turbo-0613")
 
-def predict(message, history):
-    # Reformat history for LangChain
+# 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 latest human message
+    # Add the new user message to the history
     history_langchain_format.append(HumanMessage(content=message))
-    
-    # Get response from the model
+
+    # Get the model's response
     gpt_response = llm(history_langchain_format)
-    
-    # Return response
-    return gpt_response.content
 
-# Using ChatInterface to create a chat-style UI
-demo = gr.ChatInterface(fn=predict, type="messages")
+    # 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()
@@ -129,3 +96,5 @@ if __name__ == "__main__":
 
 
 
+
+