Duplicate from ysharma/ChatinterfaceTests

Co-authored-by: yuvraj sharma <ysharma@users.noreply.huggingface.co>

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README.md

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+---
+title: ChatinterfaceTests
+emoji: 🌍
+colorFrom: red
+colorTo: gray
+sdk: gradio
+sdk_version: 3.36.1
+app_file: app.py
+pinned: false
+license: mit
+duplicated_from: ysharma/ChatinterfaceTests
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+import os
+import openai
+import gradio as gr
+from gradio import ChatInterface
+import time
+
+# Get the value of the openai_api_key from environment variable
+openai.api_key = os.getenv("OPENAI_API_KEY")
+
+# Import things that are needed generically from langchain
+from langchain import LLMMathChain, SerpAPIWrapper
+from langchain.agents import AgentType, initialize_agent, load_tools
+from langchain.chat_models import ChatOpenAI
+from langchain.tools import BaseTool, StructuredTool, Tool, tool
+from langchain.tools import MoveFileTool, format_tool_to_openai_function
+from langchain.schema import (
+    AIMessage,
+    HumanMessage,
+    SystemMessage
+)
+from langchain.utilities import WikipediaAPIWrapper
+from langchain.tools import AIPluginTool
+
+# Question- how can one set up a system message for their Chatbot while using ChatInterface
+# Example system message : system = SystemMessage(content = "You are a helpful AI assistant")
+
+# driver
+def predict_langchain(user_input, chatbot):
+
+    print(f"Chatbot : {chatbot}")
+    chat = ChatOpenAI(temperature=1.0, streaming=True, model='gpt-3.5-turbo-0613')
+    messages=[]
+
+    for conv in chatbot:
+        human = HumanMessage(content=conv[0])
+        ai = AIMessage(content=conv[1])
+        messages.append(human)
+        messages.append(ai)
+
+    messages.append(HumanMessage(content=user_input))
+
+    # getting gpt3.5's response
+    gpt_response = chat(messages)
+    return gpt_response.content
+
+def predict(inputs, chatbot):
+
+    print(f"Chatbot : {chatbot}")
+    messages = []
+    for conv in chatbot:
+        user = conv[0]
+        messages.append({"role": "user", "content":user })
+        if conv[1] is None: 
+            break
+        assistant = conv[1]
+        messages.append({"role": "assistant", "content":assistant})
+
+    # a ChatCompletion request
+    response = openai.ChatCompletion.create(
+        model='gpt-3.5-turbo',
+        messages= messages, # example :  [{'role': 'user', 'content': "What is life? Answer in three words."}],
+        temperature=1.0,
+        stream=True  # for streaming the output to chatbot
+    )
+
+    partial_message = ""
+    for chunk in response:
+        if len(chunk['choices'][0]['delta']) != 0:
+          print(chunk['choices'][0]['delta']['content'])
+          partial_message = partial_message + chunk['choices'][0]['delta']['content']
+          yield partial_message 
+
+#ChatInterface(predict, delete_last_btn="❌Delete").queue().launch(debug=True)
+
+gr.ChatInterface(predict, delete_last_btn="del").queue().launch(share=False, debug=True) #examples=["How are you?", "What's up?"],

requirements.txt

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+#pydatntic==1.*
+https://gradio-builds.s3.amazonaws.com/6257d616c40af7653f743cf9ef4f7b0a9cdabef2/gradio-3.36.1-py3-none-any.whl
+openai
+langchain