Update app.py

app.py

@@ -1,30 +1,19 @@
 import os
 import gradio as gr
-from langchain.chat_models import ChatLiteLLM
-from langchain.prompts.chat import (
-    ChatPromptTemplate,
-    SystemMessagePromptTemplate,
-    AIMessagePromptTemplate,
-    HumanMessagePromptTemplate,
-)
-from langchain.schema import AIMessage, HumanMessage, SystemMessage
+from langchain.llms import Together
 
-os.environ['PALM_API_KEY'] = "AIzaSyBcSZ82EePKjrgZphWXMpKP3u3Kcfz_8AU"  # Replace with your key
+os.environ["TOGETHERAI_API_KEY"] = "f722a9f6e3afd6b9999e6aee02aeac9e751ea3a67b124c3667ab50c85c7fa99e"  # Replace with your key
 
-chat = ChatLiteLLM(model="palm/chat-bison")
+llm = Together(
+    model="mistralai/Mistral-7B-v0.1",
+    temperature=0.7,
+    max_tokens=128,
+    top_k=1,
+    # together_api_key=""
+)
 
-def predict(message, history):
-    history_langchain_format = []
-    for human, ai in history:
-        history_langchain_format.append(HumanMessage(content=human))
-        history_langchain_format.append(AIMessage(content=ai))
-    history_langchain_format.append(HumanMessage(content=message))
+input_ = """You are a teacher with a deep knowledge of machine learning and AI. \
+You provide succinct and accurate answers. Answer the following question: 
 
-    try:
-        gpt_response = chat(history_langchain_format)
-        return gpt_response.content
-    except Exception as e:
-        print(f"An error occurred: {e}")
-        return "Sorry, I couldn't process that message."
-        
-gr.ChatInterface(predict).launch()
+What is a large language model?"""
+gr.ChatInterface(llm(input_)).launch()