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app.py

@@ -1,34 +1,34 @@
+import os
 import gradio as gr
+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
+)
 
-with gr.Blocks() as iface:
-  
-    gr.Markdown("# Mr AI")
-    gr.Markdown("Hi there! I'm your friendly assistant. Ask me anything, and I'll do my best to help! (By Thirumoorthi.A)")
+memory = ConversationBufferMemory(memory_key="chat_history")
 
-    chat_history_output = gr.Chatbot(label="Chat History", height=400)
+llm_chain = LLMChain(
+    llm=ChatOpenAI(temperature='0.5', model_name="gpt-3.5-turbo"),
+    prompt=prompt,
+    verbose=True,
+    memory=memory,
+)
 
-    
-    with gr.Row():
-        message_input = gr.Textbox(label="Your Question", placeholder="Type your question here...", lines=2)
-        submit_btn = gr.Button("Submit")
-        clear_btn = gr.Button("Clear Chat")
+def get_text_response(user_message,history):
+    response = llm_chain.predict(user_message = user_message)
+    return response
 
-    def respond(message, chat_history):
-        chat_history.append(("USER", message))  
-        response = llm_chain.run(input=message)
-        chat_history.append(("Assistant", response)) 
-        return "", chat_history  
+demo = gr.ChatInterface(get_text_response)
 
-    
-    def clear_chat():
-        return []  
-
- 
-    submit_btn.click(respond, inputs=[message_input, chat_history_output], outputs=[message_input, chat_history_output])
-    clear_btn.click(clear_chat, outputs=chat_history_output)
-
-
-iface.launch(inline=False)
+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()`.