chatbot

main.py

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+import gradio as gr
+import openai
+import os
+
+os.environ["PINECONE_API_KEY"] = "cd3256b8-4f19-4e35-b92a-1a8b32af0472"
+os.environ["PINECONE_ENV"] = "asia-southeast1-gcp-free"
+
+# Set your OpenAI GPT-3 API key
+os.environ["OPENAI_API_KEY"] = "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"
+
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.vectorstores import Pinecone
+from langchain.document_loaders.csv_loader import CSVLoader
+
+# loader = CSVLoader(file_path="products_231022 - Products.csv", encoding="utf8")
+
+# documents = loader.load()
+
+# text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+# docs = text_splitter.split_documents(documents)
+
+embeddings = OpenAIEmbeddings(openai_api_key = os.environ["OPENAI_API_KEY"])
+
+import pinecone
+
+# initialize pinecone
+pinecone.init(
+    api_key=os.getenv("PINECONE_API_KEY"),  # find at app.pinecone.io
+    environment=os.getenv("PINECONE_ENV"),  # next to api key in console
+)
+
+index_name = "chatbot"
+
+vectordb = Pinecone.from_existing_index(index_name, embeddings)
+
+from langchain.memory import ConversationBufferMemory
+from langchain.chains import ConversationalRetrievalChain
+from langchain.chat_models import ChatOpenAI
+
+# # Define a function to generate responses using GPT-3
+# def chatbot(input_text):
+
+    
+
+#     # from langchain.chat_models import ChatOpenAI
+#     # llm = ChatOpenAI(model_name='gpt-3.5-turbo', temperature=0)
+#     # llm.predict("Hello world!")
+
+#     # completion = openai.ChatCompletion.create(
+#     # model="gpt-3.5-turbo",
+#     # max_tokens=50,
+#     # api_key=api_key,
+#     # messages=[
+#     #     {"role": "user", "content": input_text}
+#     # ]
+#     # )
+#     return chain.run({'question': input_text})
+
+# # Create a Gradio interface
+# chatbot_interface = gr.Interface(
+#     fn=chatbot,
+#     inputs="text",
+#     outputs="text",
+#     title="Chatbot",
+# )
+
+# # Start the Gradio app
+# chatbot_interface.launch(share=True)
+
+
+import gradio as gr
+import openai
+import os
+
+openai.api_key = os.getenv('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')
+
+
+class Conversation:
+    def __init__(self, num_of_round):
+        self.num_of_round = num_of_round
+        self.messages = []
+
+    def ask(self, question):
+        try:
+            self.messages.append({"role": "user", "content": question})
+            retriever = vectordb.as_retriever()
+            llm = ChatOpenAI(model_name='gpt-3.5-turbo', temperature=0, openai_api_key = os.environ["OPENAI_API_KEY"])
+            memory = ConversationBufferMemory(memory_key="chat_history", return_messages= True)
+            chain = ConversationalRetrievalChain.from_llm(llm, retriever= retriever, memory= memory)
+            response = chain.run({'question': question})
+        except Exception as e:
+            print(e)
+            return e
+
+        message = response
+
+        # 最新的答案拼接进 messages
+        self.messages.append({"role": "assistant", "content": message})
+
+        if len(self.messages) > self.num_of_round*2 + 1:
+            del self.messages[1:3]  # Remove the first round conversation left.
+        return message
+
+
+
+conv = Conversation(10)
+
+
+def answer(question, history=[]):
+    history.append(question)
+    response = conv.ask(question)
+    history.append(response)
+    responses = [(u, b) for u, b in zip(history[::2], history[1::2])]
+    return responses, history
+
+
+with gr.Blocks(css="#chatbot{height:300px} .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 question and press enter")
+
+    txt.submit(answer, [txt, state], [chatbot, state])
+
+demo.launch()