Update app.py

app.py

@@ -1,64 +1,34 @@
+import os
 import gradio as gr
-from huggingface_hub import InferenceClient
+from langchain.chat_models import ChatOpenAI
+from langchain import LLMChain, PromptTemplate
+from langchain.memory import ConversationBufferMemory
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+OPENAI_API_KEY=os.gentev('OPENAI_API_KEY')
 
+template = """Meet Kookie, your youthful and witty personal assistant! At 24 years old, he's full of energy and always eager to help. Kookie's goal is to assist you with any questions or problems you might have. His enthusiasm shines through in every response, making interactions with his enjoyable and engaging
+{chat_history}
+User: {user_message}
+Chatbot:"""
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
+prompt = PromptTemplate(
+    input_variables=["chat_history","user_message"], template=template
+)
 
+memory = ConversationBufferMemory(memory_key="chat_history")
 
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
+llm_chain = LLMChain(
+    llm=ChatOpenAI(temperature='1.0', 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()
+    demo.launch() #To create a public link, set `share=True` in `launch()`. To enable errors and logs, set `debug=True` in `launch()`.