Added sentiment

app.py

@@ -2,11 +2,13 @@ import inspect
 import json
 import logging
 import os
+from typing import List, Type
+
 import gradio as gr
-from gradio import routes
 import spacy  # noqa
-from typing import List, Type
 from dotenv import load_dotenv
+from gradio import routes
+from transformers import pipeline
 
 load_dotenv()
 
@@ -50,7 +52,6 @@ def replace_chars(text, char_mapping=CHAR_MAPPING):
 
 def tokens2int(tokens, numwords={}):
     """ Convert an English str containing number words into an int
-
     >>> text2int("nine")
     9
     >>> text2int("forty two")
@@ -137,46 +138,98 @@ def get_types(cls_set: List[Type], component: str):
 
 routes.get_types = get_types
 
+functions = {
+    "text2int": text2int,
+    "text2int_preprocessed": try_text2int_preprocessed,
+}
+
+
+def text2int_selector(text, func):
+    f = functions[func]
+    return f(text)
+
+
+sentiment = pipeline(task="sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
+
+
+def get_sentiment(text):
+    return sentiment(text)
+
+
 with gr.Blocks() as html_block:
     gr.Markdown("# Gradio Blocks (3.0) with REST API")
-    textbox_input = gr.Textbox(
-        value="forty-two",
-        label="Input number words:",
-    )
+
+    inputs_text2int = [
+        gr.Text(placeholder="Type a number as text or a sentence", label="Text to process",
+                value="forty two"),
+        gr.Radio(["text2int", "text2int_preprocessed"], label="Function Selection", value="text2int")
+    ]
+
+    outputs_text2int = gr.Textbox(label="Output integer")
+
     button_text2int = gr.Button("text2int")
-    button_text2int_preprocessed = gr.Button("text2int with preprocessing")
-    textbox_output = gr.Textbox(
-        value="42",
-        label="Output integer:"
+
+    button_text2int.click(
+        fn=text2int_selector,
+        inputs=inputs_text2int,
+        outputs=outputs_text2int,
+        api_name="text2int",
     )
-    button_text2int.click(try_text2int, inputs=[textbox_input], outputs=[textbox_output])
-    button_text2int_preprocessed.click(try_text2int_preprocessed, inputs=[textbox_input], outputs=[textbox_output])
+
+    examples_text2int = [
+        ["one thousand forty seven", "text2int"],
+        ["one hundred", "text2int_preprocessed"],
+    ]
+
+    gr.Examples(examples=examples_text2int, inputs=inputs_text2int)
+
+    inputs_sentiment = [
+        gr.Text(placeholder="Type a number as text or a sentence", label="Text to process",
+                value="I really like it!"),
+    ]
+
+    outputs_sentiment = gr.Textbox(label="Sentiment result")
+
+    button_sentiment = gr.Button("sentiment analysis")
+
+    button_sentiment.click(
+        get_sentiment,
+        inputs=inputs_sentiment,
+        outputs=outputs_sentiment,
+        api_name="sentiment-analysis"
+    )
+
+    examples_sentiment = [
+        ["Couldn't agree more!"],
+        ["Sorry, I can not accept this!"],
+    ]
+
+    gr.Examples(examples=examples_sentiment, inputs=inputs_sentiment)
+
     gr.Markdown(r"""
 
 ## API
 
-You can select which function to run using the `fn_index` argument:
-
 ```python
 import requests
 
 requests.post(
-    url="https://Hobson-gradio-rest-api.hf.space/api/predict/", json={"data": ["one hundred forty-two"], "fn_index": 0}
+    url="https://tangibleai-mathtext.hf.space/run/text2int", json={"data": ["one hundred forty five", "text2int"]}
 ).json()
 ```
 
 Or using `curl`:
 
 ```bash
-curl -X POST https://Hobson-gradio-rest-api.hf.space/api/predict/ -H 'Content-Type: application/json' -d '{"data": ["one hundred forty-two"], "fn_index": 0}'
+curl -X POST https://tangibleai-mathtext.hf.space/run/text2int -H 'Content-Type: application/json' -d '{"data": ["one hundred forty five", "text2int"]}'
 ```
 """ + f"{json.loads(BQ_JSON)['type']}")
 
-interface = gr.Interface(lambda: None, inputs=[textbox_input], outputs=[textbox_output])
+# interface = gr.Interface(lambda x: x, inputs=["text"], outputs=["text"])
+# html_block.input_components = interface.input_components
+# html_block.output_components = interface.output_components
+# html_block.examples = None
 
-html_block.input_components = interface.input_components
-html_block.output_components = interface.output_components
-html_block.examples = None
 html_block.predict_durations = []
 
-bapp = html_block.launch()
+html_block.launch()

requirements.txt

@@ -1,5 +1,7 @@
 spacy
 pandas
 pandas-gbq
-gradio>=3.0.2,<3.1.0
+gradio==3.14.0
 python-dotenv
+transformers
+torch

test_api.py

@@ -1,46 +1,63 @@
 """https://zetcode.com/python/concurrent-http-requests/"""
 
-
 import asyncio
 import random
 import time
 
 import httpx
 
-local_url = "http://127.0.0.1:5000"
-remote_url = "https://cetinca-mathtext-nlu.hf.space/run/text2int_preprocessed"
-remote_url = "https://tangibleai-mathtext.hf.space/run/"
 headers = {"Content-Type": "application/json; charset=utf-8"}
-data_list = [
-    ["one hundred forty five"],
-    ["ninety nine"],
-    ["seventy three"],
-    ["one thousand seven hundred sixty nine"],
+
+data_list_local = [
+    {"url": "http://127.0.0.1:7860/run/text2int", "data": ["one hundred forty five", "text2int"]},
+    {"url": "http://127.0.0.1:7860/run/text2int", "data": ["twenty thousand nine hundred fifty", "text2int_preprocessed"]},
+    {"url": "http://127.0.0.1:7860/run/text2int", "data": ["one hundred forty five", "text2int"]},
+    {"url": "http://127.0.0.1:7860/run/text2int", "data": ["nine hundred eighty three", "text2int_preprocessed"]},
+    {"url": "http://127.0.0.1:7860/run/text2int", "data": ["five million"]},
+    {"url": "http://127.0.0.1:7860/run/sentiment-analysis", "data": ["Totally agree"]},
+    {"url": "http://127.0.0.1:7860/run/sentiment-analysis", "data": ["I like it"]},
+    {"url": "http://127.0.0.1:7860/run/sentiment-analysis", "data": ["No more"]},
+    {"url": "http://127.0.0.1:7860/run/sentiment-analysis", "data": ["I am not sure"]},
+    {"url": "http://127.0.0.1:7860/run/sentiment-analysis", "data": ["Never"]},
+]
+
+data_remote = [
+    {"url": "https://tangibleai-mathtext.hf.space/run/text2int", "data": ["one hundred forty five", "text2int"]},
+    {"url": "https://tangibleai-mathtext.hf.space/run/text2int", "data": ["twenty thousand nine hundred fifty", "text2int_preprocessed"]},
+    {"url": "https://tangibleai-mathtext.hf.space/run/text2int", "data": ["one hundred forty five", "text2int"]},
+    {"url": "https://tangibleai-mathtext.hf.space/run/text2int", "data": ["nine hundred eighty three", "text2int_preprocessed"]},
+    {"url": "https://tangibleai-mathtext.hf.space/run/text2int", "data": ["five million"]},
+    {"url": "https://tangibleai-mathtext.hf.space/run/sentiment-analysis", "data": ["Totally agree"]},
+    {"url": "https://tangibleai-mathtext.hf.space/run/sentiment-analysis", "data": ["I like it"]},
+    {"url": "https://tangibleai-mathtext.hf.space/run/sentiment-analysis", "data": ["No more"]},
+    {"url": "https://tangibleai-mathtext.hf.space/run/sentiment-analysis", "data": ["I am not sure"]},
+    {"url": "https://tangibleai-mathtext.hf.space/run/sentiment-analysis", "data": ["Never"]},
 ]
 
 
 # async call to endpoint
-async def call_api(url, _data_list, number):
-    data = random.choice(_data_list)
-    json = {"data": data, "fn_index": 1}
+async def call_api(url, data, number):
+    json = {"data": data}
     async with httpx.AsyncClient() as client:
-        begin1 = time.time()
-        begin2 = time.perf_counter()  # Used perf_counter for more precise result.
-        # print(f"Call {number} started on: {start} text: {data}")
+        start = time.perf_counter()  # Used perf_counter for more precise result.
         response = await client.post(url=url, headers=headers, json=json, timeout=30)
-        print(response.status_code)
-        end1 = time.time()
-        end2 = time.perf_counter()
-        return f"Call_{number}: start: {begin1} end: {end1} delay: {end1 - begin1} with_time: {end2 - begin2}\n"\
-               f"input_text: {data} result: {response.json().get('data')}"
+        end = time.perf_counter()
+        # print(response.status_code)
+        return f"Call_{number}\n" \
+               f"start: {start:.4f} end: {end:.4f} delay: {(end - start):.4f}\n" \
+               f"input_text: {data}\n" \
+               f"result: {response.json().get('data')}"
 
 
 async def main(n):
     calls = []
     for num in range(n):
-        calls.append(call_api(remote_url, data_list, num))
+        item = random.choice(data_remote)
+        url, data = item["url"], item["data"]
+        # calls.append(call_api(remote_url, data_list, num))
+        calls.append(call_api(url, data, num))
     r = await asyncio.gather(*calls)
     print(*r, sep="\n")
 
 
-asyncio.run(main(1))
+asyncio.run(main(30))