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.gitattributes

@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+index.json filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,56 @@
+# Import packages
+import openai
+from llama_index import SimpleDirectoryReader, GPTListIndex, GPTSimpleVectorIndex, LLMPredictor, PromptHelper, ServiceContext
+from langchain.chat_models import ChatOpenAI
+import gradio as gr
+import sys
+import os
+import PyPDF2
+
+#os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
+# OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+
+'''
+def construct_index(directory_path):
+    max_input_size = 4096
+    num_outputs = 512
+    max_chunk_overlap = 20
+    chunk_size_limit = 600
+    prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit)
+    llm_predictor = LLMPredictor(llm=ChatOpenAI(temperature=0.7, model_name="gpt-3.5-turbo", max_tokens=num_outputs))
+    documents = SimpleDirectoryReader(directory_path).load_data()
+    service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, prompt_helper=prompt_helper)
+    index = GPTSimpleVectorIndex.from_documents(documents, service_context=service_context)
+    index.save_to_disk('index.json')
+    return index
+'''
+
+def chatbot(input_text, openai_api_key):
+    os.environ["OPENAI_API_KEY"] = openai_api_key
+    index = GPTSimpleVectorIndex.load_from_disk('index.json')
+    response = index.query(input_text, response_mode="compact")
+    return response.response
+
+# chat = gr.Interface(fn=chatbot,
+#                     inputs=gr.components.Textbox(lines=7, label="Ask your question to ChatGPT"),
+#                     outputs="text",
+#                     title="Custom-trained AI Chatbot for employee tax assessment 2022")
+
+# Documentation how to make gradio interfaces: https://gradio.app/quickstart/
+
+with gr.Blocks() as chat:
+
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown("""## Trained with custom data""",
+                    elem_id="header")
+
+    with gr.Column():
+        gr.Markdown("Enter your OpenAI API Key.")
+        openai_api_key = gr.Textbox(value='', placeholder="OpenAI API Key", type="password", label="Enter OpenAI API Key")
+    
+    text_input = gr.Textbox(lines=7, label="Enter your question")
+    output = gr.Textbox(label="Response")
+    greet_btn = gr.Button("Generate Response")
+    greet_btn.click(fn=chatbot, inputs=[text_input, openai_api_key], outputs=output)
+
+chat.launch()

index.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:961ab7beda74080c295444752b998849120fee441604786fc7736ab560210cd9
+size 14998715

requirements.txt

@@ -0,0 +1,5 @@
+openai
+llama-index
+gradio
+PyPDF2
+PyCryptodome