Update app.py

app.py

@@ -1,58 +1,46 @@
-import openai
-import os
+from gpt_index import SimpleDirectoryReader, GPTListIndex, GPTSimpleVectorIndex, LLMPredictor, PromptHelper
+#from langchain.schema import BaseLanguageModel
+from langchain.chat_models import ChatOpenAI
 import gradio as gr
+import sys
+import os
+import openai
 
-openai.api_key = os.environ.get("OPENAI_API_KEY")
-
-class Conversation:
-    def __init__(self, prompt, num_of_round):
-        self.prompt = prompt
-        self.num_of_round = num_of_round
-        self.messages = []
-        self.messages.append({"role": "system", "content": self.prompt})
-
-    def ask(self, question):
-        try:
-            self.messages.append( {"role": "user", "content": question})
-            response = openai.ChatCompletion.create(
-                model="gpt-3.5-turbo",
-                messages=self.messages,
-                temperature=0.5,
-                max_tokens=2048,
-                top_p=1,
-            )
-        except Exception as e:
-            print(e)
-            return e
-
-        message = response["choices"][0]["message"]["content"]
-        self.messages.append({"role": "assistant", "content": message})
-        
-        if len(self.messages) > self.num_of_round*2 + 1:
-            del self.messages[1:3]
-        return message
-    
+os.environ["OPENAI_API_KEY"] = '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'
+
+messages = [
+    {"role": "system", "content": "You try to be secretive but are terrible at it."},
+]
 
-prompt = """你是一个中国厨师，用中文回答做菜的问题。你的回答需要满足以下要求:
-1. 你的回答必须是中文
-2. 回答限制在100个字以内"""
+def construct_index(directory_path):
+    max_input_size = 4096
+    num_outputs = 512
+    max_chunk_overlap = 20
+    chunk_size_limit = 600
 
-conv = Conversation(prompt, 5)
+    prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit)
 
-def predict(input, history=[]):
-    history.append(input)
-    response = conv.ask(input)
-    history.append(response)
-    responses = [(u,b) for u,b in zip(history[::2], history[1::2])]
-    return responses, history
+    llm_predictor = LLMPredictor(llm=ChatOpenAI(temperature=0.7, model_name="gpt-3.5-turbo", max_tokens=num_outputs))
+
+    documents = SimpleDirectoryReader(directory_path).load_data()
+
+    index = GPTSimpleVectorIndex(documents, llm_predictor=llm_predictor, prompt_helper=prompt_helper)
+
+    index.save_to_disk('index.json')
+
+    return index
+    
 
-with gr.Blocks(css="#chatbot{height:350px} .overflow-y-auto{height:500px}") as demo:
-    chatbot = gr.Chatbot(elem_id="chatbot")
-    state = gr.State([])
 
-    with gr.Row():
-        txt = gr.Textbox(show_label=False, placeholder="Enter text and press enter").style(container=False)
+def chatbot(input_text):
+    index = GPTSimpleVectorIndex.load_from_disk('index.json')
+    response = index.query(input_text, response_mode="compact")
+    return response.response
 
-    txt.submit(predict, [txt, state], [chatbot, state])
+iface = gr.Interface(fn=chatbot,
+                     inputs=gr.components.Textbox(lines=7, label="Hello, I'm a wiG, what would you like to know?"),
+                     outputs="text",
+                     title="wiGs explained custom-trained AI Chatbot")
 
-demo.launch()
+index = construct_index("docs")
+iface.launch(share=True)