Update app.py

app.py

@@ -42,47 +42,80 @@
 # if __name__ == "__main__":
 #     demo.launch()
 
-import os
+# 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 gradio as gr
 from langchain.chat_models import ChatOpenAI
 from langchain.schema import AIMessage, HumanMessage
+import openai
 
-# Set OpenAI API Key
-os.environ["OPENAI_API_KEY"] = "sk-3_mJiR5z9Q3XN-D33cgrAIYGffmMvHfu5Je1U0CW1ZT3BlbkFJA2vfSvDqZAVUyHo2JIcU91XPiAq424OSS8ci29tWMA"  # Replace with your key
+os.environ["OPENAI_API_KEY"] = "sk-3_mJiR5z9Q3XN-D33cgrAIYGffmMvHfu5Je1U0CW1ZT3BlbkFJA2vfSvDqZAVUyHo2JIcU91XPiAq424OSS8ci29tWMA" 
 
-# Initialize the ChatOpenAI model
-llm = ChatOpenAI(temperature=1.0, model="gpt-3.5-turbo-0613")
+# Initialize ChatOpenAI
+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
+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 the new user message to the history
+    # Add latest human message
     history_langchain_format.append(HumanMessage(content=message))
-
-    # Get the model's response
+    
+    # Get response from the model
     gpt_response = llm(history_langchain_format)
+    
+    # Return response
+    return gpt_response.content
 
-    # 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"]
-)
+# Using ChatInterface to create a chat-style UI
+demo = gr.ChatInterface(fn=predict, type="messages")
 
 if __name__ == "__main__":
     demo.launch()
@@ -96,4 +129,3 @@ if __name__ == "__main__":
 
 
 
-