Update test_api

.gitignore

@@ -98,3 +98,4 @@ docs/**/*.html
 .bash_env
 **/*secret*
 **/*private*
+/call_history.csv

app.py

@@ -138,17 +138,6 @@ 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")
 
 
@@ -157,73 +146,133 @@ def get_sentiment(text):
 
 
 with gr.Blocks() as html_block:
-    gr.Markdown("# Gradio Blocks (3.0) with REST API")
-
-    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.click(
-        fn=text2int_selector,
-        inputs=inputs_text2int,
-        outputs=outputs_text2int,
-        api_name="text2int",
-    )
-
-    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"""
+    gr.Markdown("# Rori - Mathbot")
 
-## API
+    with gr.Tab("Text to integer"):
+        inputs_text2int = [
+            gr.Text(placeholder="Type a number as text or a sentence", label="Text to process",
+                    value="forty two"),
+        ]
 
-```python
-import requests
+        outputs_text2int = gr.Textbox(label="Output integer")
 
-requests.post(
-    url="https://tangibleai-mathtext.hf.space/run/text2int", json={"data": ["one hundred forty five", "text2int"]}
-).json()
-```
+        button_text2int = gr.Button("text2int")
 
-Or using `curl`:
+        button_text2int.click(
+            fn=try_text2int,
+            inputs=inputs_text2int,
+            outputs=outputs_text2int,
+            api_name="text2int",
+        )
 
-```bash
-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']}")
+        examples_text2int = [
+            "one thousand forty seven",
+            "one hundred",
+        ]
+
+        gr.Examples(examples=examples_text2int, inputs=inputs_text2int)
+
+        gr.Markdown(r"""
+
+        ## API
+        ```python
+        import requests
+
+        requests.post(
+            url="https://tangibleai-mathtext.hf.space/run/text2int", json={"data": ["one hundred forty five"]}
+        ).json()
+        ```
+
+        Or using `curl`:
+
+        ```bash
+        curl -X POST https://tangibleai-mathtext.hf.space/run/text2int -H 'Content-Type: application/json' -d '{"data": ["one hundred forty five"]}'
+        ```
+        {bq_json}""" + f"{json.loads(BQ_JSON)['type']}")
+
+    with gr.Tab("Text to integer preprocessed"):
+        inputs_text2int_preprocessed = [
+            gr.Text(placeholder="Type a number as text or a sentence", label="Text to process",
+                    value="forty two"),
+        ]
+
+        outputs_text2int_preprocessed = gr.Textbox(label="Output integer")
+
+        button_text2int = gr.Button("text2int preprocessed")
+
+        button_text2int.click(
+            fn=try_text2int_preprocessed,
+            inputs=inputs_text2int_preprocessed,
+            outputs=outputs_text2int_preprocessed,
+            api_name="text2int_preprocessed",
+        )
+
+        examples_text2int_preprocessed = [
+            "one thousand forty seven",
+            "one hundred",
+        ]
+
+        gr.Examples(examples=examples_text2int_preprocessed, inputs=inputs_text2int_preprocessed)
+
+        gr.Markdown(r"""
+
+        ## API
+        ```python
+        import requests
+
+        requests.post(
+            url="https://tangibleai-mathtext.hf.space/run/text2int_preprocessed", json={"data": ["one hundred forty five"]}
+        ).json()
+        ```
+
+        Or using `curl`:
+
+        ```bash
+        curl -X POST https://tangibleai-mathtext.hf.space/run/text2int_preprocessed -H 'Content-Type: application/json' -d '{"data": ["one hundred forty five"]}'
+        ```
+        {bq_json}""" + f"{json.loads(BQ_JSON)['type']}")
+
+    with gr.Tab("Sentiment Analysis"):
+        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 = [
+            ["Totally agree!"],
+            ["Sorry, I can not accept this!"],
+        ]
+
+        gr.Examples(examples=examples_sentiment, inputs=inputs_sentiment)
+
+        gr.Markdown(r"""
+    
+        ## API
+        ```python
+        import requests
+        
+        requests.post(
+            url="https://tangibleai-mathtext.hf.space/run/sentiment-analysis", json={"data": ["You are right!"]}
+        ).json()
+        ```
+        
+        Or using `curl`:
+        
+        ```bash
+        curl -X POST https://tangibleai-mathtext.hf.space/run/sentiment-analysis -H 'Content-Type: application/json' -d '{"data": ["You are right!"]}'
+        ```
+        {bq_json}""" + f"{json.loads(BQ_JSON)['type']}")
 
 # interface = gr.Interface(lambda x: x, inputs=["text"], outputs=["text"])
 # html_block.input_components = interface.input_components

plot_calls.py

@@ -0,0 +1,9 @@
+import matplotlib.pyplot as plt
+import pandas as pd
+
+df = pd.read_csv('call_history.csv')  # data loading
+print(df)
+
+df.plot(by='endpoint', column='delay', kind='box', showmeans=True)
+
+plt.show()

requirements.txt

@@ -5,3 +5,4 @@ gradio==3.14.0
 python-dotenv
 transformers
 torch
+httpx

test_api.py

@@ -3,61 +3,93 @@
 import asyncio
 import random
 import time
-
+import pandas as pd
 import httpx
+from os.path import exists
+
+NUMBER_OF_CALLS = 10
 
 headers = {"Content-Type": "application/json; charset=utf-8"}
 
-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"]},
-]
+base_url = "https://tangibleai-mathtext.hf.space/run/{endpoint}"
+# base_url = "http://localhost:7860/run/{endpoint}"
+
+data_list_1 = {
+    "endpoint": "text2int",
+    "test_data": [
+        "one hundred forty five",
+        "twenty thousand nine hundred fifty",
+        "one hundred forty five",
+        "nine hundred eighty three",
+        "five million",
+    ]
+}
+
+data_list_2 = {
+    "endpoint": "text2int_preprocessed",
+    "test_data": [
+        "one hundred forty five",
+        "twenty thousand nine hundred fifty",
+        "one hundred forty five",
+        "nine hundred eighty three",
+        "five million",
+    ]
+}
+data_list_3 = {
+    "endpoint": "sentiment-analysis",
+    "test_data": [
+        "Totally agree",
+        "I like it",
+        "No more",
+        "I am not sure",
+        "Never",
+    ]
+}
 
 
 # async call to endpoint
-async def call_api(url, data, number):
-    json = {"data": data}
+async def call_api(url, data, call_number, number_of_calls):
+    json = {"data": [data]}
     async with httpx.AsyncClient() as client:
         start = time.perf_counter()  # Used perf_counter for more precise result.
         response = await client.post(url=url, headers=headers, json=json, timeout=30)
         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')}"
+        return {
+            "endpoint": url.split("/")[-1],
+            "test data": data,
+            "response": response.json().get("data"),
+            "call number": call_number,
+            "number of calls": number_of_calls,
+            "start": start.__round__(4),
+            "end": end.__round__(4),
+            "delay": (end - start).__round__(4)
+        }
+
+
+data_lists = [data_list_1, data_list_2, data_list_3]
+
+results = []
+
+
+async def main(number_of_calls):
+    for data_list in data_lists:
+        calls = []
+        for call_number in range(1, number_of_calls + 1):
+            url = base_url.format(endpoint=data_list["endpoint"])
+            data = random.choice(data_list["test_data"])
+            calls.append(call_api(url, data, call_number, number_of_calls))
+        r = await asyncio.gather(*calls)
+        results.extend(r)
 
 
-async def main(n):
-    calls = []
-    for num in range(n):
-        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")
 
+start = time.perf_counter()
+asyncio.run(main(NUMBER_OF_CALLS))
+end = time.perf_counter()
+print(end-start)
+df = pd.DataFrame(results)
 
-asyncio.run(main(30))
+if exists("call_history.csv"):
+    df.to_csv(path_or_buf="call_history.csv", mode="a", header=False, index=False)
+else:
+    df.to_csv(path_or_buf="call_history.csv", mode="w", header=True, index=False)