from omegaconf import OmegaConf
from query import VectaraQuery
import os
import gradio as gr

def isTrue(x) -> bool:
    if isinstance(x, bool):
        return x
    return x.strip().lower() == 'true'

corpus_keys = str(os.environ['corpus_keys']).split(',')
cfg = OmegaConf.create({
    'corpus_keys': corpus_keys,
    'api_key': str(os.environ['api_key']),
    'title': os.environ['title'],
    'source_data_desc': os.environ['source_data_desc'],
    'streaming': isTrue(os.environ.get('streaming', False)),
    'prompt_name': os.environ.get('prompt_name', None),
    'examples': os.environ.get('examples', None)
})

vq = VectaraQuery(cfg.api_key, cfg.corpus_keys, cfg.prompt_name)

def respond(message, history):
    if cfg.streaming:
        # Call stream response and stream output
        stream = vq.submit_query_streaming(message)
        
        
        outputs = ""
        for output in stream:
            outputs += output
            yield outputs
    else:
        # Call non-stream response and return message output
        response = vq.submit_query(message)
        yield response

def vote(data: gr.LikeData):
    if data.liked:
        print("Received Thumbs up")
    else:
        print("Received Thumbs down")

heading_html = f'''
                <table>
                  <tr>
                      <td style="width: 80%; text-align: left; vertical-align: middle;"> <h1>Vectara AI Assistant: {cfg.title}</h1> </td>
                      <td style="width: 20%; text-align: right; vertical-align: middle;"> <img src="https://github.com/david-oplatka/chatbot-streamlit/blob/main/Vectara-logo.png?raw=true"> </td>
                  </tr>
                  <tr>
                      <td colspan="2" style="font-size: 16px;">This demo uses Retrieval Augmented Generation from <a href="https://vectara.com/">Vectara</a> to ask questions about {cfg.source_data_desc}.</td>
                  </tr>
                </table>
                '''

bot_css = """
table {
  border: none;
  width: 100%;
  table-layout: fixed;
  border-collapse: separate;
}
td {
  vertical-align: middle;
  border: none;
}
img {
  width: 75%;
}
h1 {
  font-size: 2em; /* Adjust the size as needed */
}
"""

if cfg.examples:
    app_examples = [example.strip() for example in cfg.examples.split(",")]
else:
    app_examples = None

with gr.Blocks() as demo:
    chatbot = gr.Chatbot(value = [[None, "How may I help you?"]])
    chatbot.like(vote, None, None)
    gr.ChatInterface(respond, description = heading_html, css = bot_css,
                        chatbot = chatbot, examples = app_examples, cache_examples = False)

if __name__ == "__main__":
    demo.launch()


# from omegaconf import OmegaConf
# from query import VectaraQuery
# import os
# import gradio as gr

# def isTrue(x) -> bool:
#     if isinstance(x, bool):
#         return x
#     return x.strip().lower() == 'true'

# corpus_keys = str(os.environ['corpus_keys']).split(',')
# cfg = OmegaConf.create({
#     'corpus_keys': corpus_keys,
#     'api_key': str(os.environ['api_key']),
#     'title': os.environ['title'],
#     'source_data_desc': os.environ['source_data_desc'],
#     'streaming': isTrue(os.environ.get('streaming', False)),
#     'prompt_name': os.environ.get('prompt_name', None),
#     'examples': os.environ.get('examples', None)
# })

# vq = VectaraQuery(cfg.api_key, cfg.corpus_keys, cfg.prompt_name)

# def respond(message, history):
#     if cfg.streaming:
#         # Call stream response and stream output
#         stream = vq.submit_query_streaming(message)
#         for output in stream:
#             yield output
#     else:
#         # Call non-stream response and return message output
#         response = vq.submit_query(message)
#         yield response

# def vote(data: gr.LikeData):
#     if data.liked:
#         print("Received Thumbs up")
#     else:
#         print("Received Thumbs down")

# heading_html = f'''
# <table>
#     <tr>
#         <td style="width: 80%; text-align: left; vertical-align: middle;">
#             <h1>Vectara AI Assistant: {cfg.title}</h1>
#         </td>
#         <td style="width: 20%; text-align: right; vertical-align: middle;">
#             <img src="https://github.com/david-oplatka/chatbot-streamlit/blob/main/Vectara-logo.png?raw=true">
#         </td>
#     </tr>
#     <tr>
#         <td colspan="2" style="font-size: 16px;">This demo uses Retrieval Augmented Generation from <a href="https://vectara.com/">Vectara</a> to ask questions about {cfg.source_data_desc}.</td>
#     </tr>
# </table>
# '''

# bot_css = """
# table { border: none; width: 100%; table-layout: fixed; border-collapse: separate;}
# td { vertical-align: middle; border: none;}
# img { width: 75%;}
# h1 { font-size: 2em; /* Adjust the size as needed */}
# """

# if cfg.examples:
#     app_examples = [example.strip() for example in cfg.examples.split(",")]
# else:
#     app_examples = None

# with gr.Blocks(css=bot_css) as demo:
#     gr.HTML(heading_html)
#     chatbot = gr.Chatbot(value=[[None, "How may I help you?"]])
#     msg = gr.Textbox(label="Message")
#     clear = gr.Button("Clear")

#     def user(message, history):
#         return "", history + [[message, None]]

#     def bot(history):
#         message = history[-1][0]
#         bot_message = respond(message, history)

#         if cfg.streaming:
#             full_response = ""
#             for chunk in bot_message:
#                 full_response += chunk
#                 history[-1][1] = full_response
#                 yield history
#         else:
#             history[-1][1] = next(bot_message)
#             yield history

#     msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
#         bot, chatbot, chatbot, api_name="bot_response"
#     )
#     chatbot.like(vote, None, None)

#     clear.click(lambda: None, None, chatbot, queue=False)

#     if app_examples:
#         gr.Examples(
#             app_examples,
#             inputs=msg,
#             outputs=chatbot,
#             fn=user,
#             cache_examples=False
#         )

# if __name__ == "__main__":
#     demo.launch()

    

# from omegaconf import OmegaConf
# from query import VectaraQuery
# import os
# import gradio as gr

# def isTrue(x) -> bool:
#     if isinstance(x, bool):
#         return x
#     return x.strip().lower() == 'true'

# corpus_keys = str(os.environ['corpus_keys']).split(',')
# cfg = OmegaConf.create({
#     'corpus_keys': corpus_keys,
#     'api_key': str(os.environ['api_key']),
#     'title': os.environ['title'],
#     'source_data_desc': os.environ['source_data_desc'],
#     'streaming': isTrue(os.environ.get('streaming', False)),
#     'prompt_name': os.environ.get('prompt_name', None),
#     'examples': os.environ.get('examples', None)
# })

# vq = VectaraQuery(cfg.api_key, cfg.corpus_keys, cfg.prompt_name)

# def respond(message, history):
#     if cfg.streaming:
#         # Call stream response and stream output
#         stream = vq.submit_query_streaming(message)
        
        
#         outputs = ""
#         for output in stream:
#             outputs += output
#             yield outputs
#     else:
#         # Call non-stream response and return message output
#         response = vq.submit_query(message)
#         yield response

# heading_html = f'''
#                 <table>
#                   <tr>
#                       <td style="width: 80%; text-align: left; vertical-align: middle;"> <h1>Vectara AI Assistant: {cfg.title}</h1> </td>
#                       <td style="width: 20%; text-align: right; vertical-align: middle;"> <img src="https://github.com/david-oplatka/chatbot-streamlit/blob/main/Vectara-logo.png?raw=true"> </td>
#                   </tr>
#                   <tr>
#                       <td colspan="2" style="font-size: 16px;">This demo uses Retrieval Augmented Generation from <a href="https://vectara.com/">Vectara</a> to ask questions about {cfg.source_data_desc}.</td>
#                   </tr>
#                 </table>
#                 '''

# bot_css = """
# table {
#   border: none;
#   width: 100%;
#   table-layout: fixed;
#   border-collapse: separate;
# }
# td {
#   vertical-align: middle;
#   border: none;
# }
# img {
#   width: 75%;
# }
# h1 {
#   font-size: 2em; /* Adjust the size as needed */
# }
# """

# if cfg.examples:
#     app_examples = [example.strip() for example in cfg.examples.split(",")]
# else:
#     app_examples = None

# demo = gr.ChatInterface(respond, description = heading_html, css = bot_css,
#                         chatbot = gr.Chatbot(value = [[None, "How may I help you?"]]), examples = app_examples, cache_examples = False)

# if __name__ == "__main__":
#     demo.launch()