Upload app.py

app.py

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+import os
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.vectorstores import Chroma
+from langchain.prompts import ChatPromptTemplate
+from langchain.chat_models import ChatOpenAI
+import gradio as gr
+
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") # add your OpenAI API Key
+# for this example I used Alphabet Inc 10-K Report 2022
+# https://www.sec.gov/Archives/edgar/data/1652044/000165204423000016/goog-20221231.htm
+
+embeddings = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY)
+
+db_chroma = Chroma(persist_directory="./chroma", embedding_function=embedding)
+
+PROMPT_TEMPLATE = """
+Answer the question based only on the following context:
+{context}
+
+Provide a detailed answer.
+Don’t justify your answers.
+Don’t give information not mentioned in the CONTEXT INFORMATION.
+Do not say "according to the context" or "mentioned in the context" or similar.
+
+**Important: Respond in the same language as the question.**
+- If the question is asked in English, your response must be in English.
+- Si la pregunta está formulada en español, tu respuesta debe ser en español.
+
+Question/Pregunta:
+{question}."""
+
+prompt_template = ChatPromptTemplate.from_template(PROMPT_TEMPLATE)
+
+model = ChatOpenAI(api_key=OPENAI_API_KEY)
+
+def get_response(query,history):
+  docs_chroma = db_chroma.similarity_search_with_score(query, k=5)
+
+  # generate an answer based on given user query and retrieved context information
+  context_text = "\n\n".join([doc.page_content for doc, _score in docs_chroma])
+
+  prompt = prompt_template.format(context=context_text, question=query)
+
+  response_text = model.predict(prompt)
+  return response_text
+
+
+gr.ChatInterface(fn=get_response,examples=['CI/CD', 'Kuberflow Pipeline', 'Cloud Build Service', 'TensorFlow Extended (TFX)'],title='Machine Learning Certification Chatbot').launch(debug=True)