File size: 4,816 Bytes
c00ccba
 
5b962b1
88b47df
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
bfb4523
88b47df
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0cd32cd
bfb4523
0cd32cd
 
6cc3988
27bf09a
 
0cd32cd
 
 
 
 
 
3beb245
88b47df
 
 
6cc3988
 
 
 
88b47df
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
af75af6
88b47df
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3beb245
88b47df
 
3beb245
88b47df
 
3beb245
88b47df
 
c697f2d
91442fc
88b47df
 
3beb245
88b47df
 
3beb245
88b47df
 
 
 
3beb245
 
88b47df
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
#import json
import os
import pprint
#import shutil
#import requests

import gradio as gr

from transformers.utils import logging
from langchain.embeddings import HuggingFaceInstructEmbeddings, GooglePalmEmbeddings
import pinecone
from langchain.vectorstores import Pinecone

logging.set_verbosity_debug()

instructor_embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl", model_kwargs={"device": "cpu"})

HF_TOKEN = os.environ.get("HF_TOKEN", None)
PINECONE_API_KEY = os.environ.get("PINECONE_API_KEY", None)
PINECONE_ENV = os.environ.get("PINECONE_ENV", None)
GOOGLE_API_KEY = os.environ.get("GOOGLE_API_KEY", None)

pinecone.init(api_key=PINECONE_API_KEY, environment=PINECONE_ENV)

from langchain.llms import GooglePalm
from langchain.chains import RetrievalQAWithSourcesChain
llm=GooglePalm(google_api_key=GOOGLE_API_KEY, temperature=0.1, max_output_tokens=2048)
vectorStore = Pinecone.from_existing_index('tennis', instructor_embeddings)
retriever = vectorStore.as_retriever(search_kwargs={"k": 3})
qa_chain_instrucEmbed = RetrievalQAWithSourcesChain.from_chain_type(llm=llm,
                                  chain_type="stuff",
                                  retriever=retriever,
                                  return_source_documents=True,
                                  verbose=True
                                  )

theme = gr.themes.Monochrome(
    primary_hue="indigo",
    secondary_hue="blue",
    neutral_hue="slate",
    radius_size=gr.themes.sizes.radius_sm,
    font=[
        gr.themes.GoogleFont("Open Sans"),
        "ui-sans-serif",
        "system-ui",
        "sans-serif",
    ],
)

def generate(question):
    ret = qa_chain_instrucEmbed(question)
    pprint.pprint(ret)
    answer = ret['answer']
    sources = ret['sources']
    embed_video_html = '<div>'
    if sources is not None and len(sources) > 0:
        sources = [s.strip() for s in sources.split(',')]
        for source in sources:
            embed_video_html += f'''
                <iframe width="560" height="315" src="https://www.youtube.com/embed/{source}"
                title="YouTube video player" frameborder="0" allow="accelerometer; autoplay;
                clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
                '''
    return answer, embed_video_html+'</div>'

examples = [
    "Tell me step by step how to find out my dominant eye when I play tennis.",
    "What do we look for in a great tennis player? Write out the essential attributes.",
    "Who has the best tennis serve? Explain in details.",
    "Compare Novak and Nadal gamestyle in details. Who is better?",
    "Who is the tennis GOAT?"
]

def process_example(args):
    for x in generate(args):
        pass
    return x


css = ".generating {visibility: hidden}"

monospace_css = """
#q-input textarea {
    font-family: monospace, 'Consolas', Courier, monospace;
}
"""

css += monospace_css + ".gradio-container {color: black}"

description = """
<div style="text-align: center;">
    <h1>Ask Coach Patrick Mouratoglou</h1>
</div>
<div style="text-align: left;">
    <p>This is a demo to answer some popular questions from tennis fans to Coach Patrick. The information is being extracted from his official <a href="https://www.youtube.com/@patrickmouratoglou_official" style='color: #e6b800;'>Youtube channel</a>. It's using the following technologies:</p>
    <ul>
        <li>Google PALM</li>
        <li>Gradio</li>
        <li>hkunlp/instructor-xl</li>
        <li>HuggingFace</li>
        <li>Langchain</li>
        <li>Pinecone</li>
    </ul>
</div>
"""
disclaimer = """⚠️<b>This is an unofficial website.</b>\
 <br>**Intended Use**: this app for demonstration purposes; not to serve as replacement for Coach Patrick official media channels or personal expertise."""

with gr.Blocks(theme=theme, analytics_enabled=False, css=css) as demo:
    with gr.Column():
        gr.Markdown(description)
        gr.Markdown(disclaimer)
        with gr.Row():
            with gr.Column():
                question = gr.Textbox(
                    placeholder="Enter your question here",
                    lines=5,
                    label="Question"
                )
                submit = gr.Button("Ask", variant="primary")
                output = gr.Textbox(elem_id="q-output", lines=10, label="Answer")
                video = gr.HTML('')
                gr.Examples(
                    examples=examples,
                    inputs=[question],
                    cache_examples=False,
                    fn=process_example,
                    outputs=[output, video],
                )

    submit.click(
        generate,
        inputs=[question],
        outputs=[output, video],
    )
demo.queue(concurrency_count=16).launch(debug=True)