add fastapi endpoint

.gitignore

@@ -0,0 +1,3 @@
+__pycache__
+.venv
+flagged

app.py

@@ -1,6 +1,7 @@
 import re
 
 import gradio as gr
+from pydantic import BaseModel
 from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM
 from loguru import logger
 
@@ -44,6 +45,21 @@ DEFAULT_RU_TEXT = """В результате взрыва на заправке,
 доноров для их пополнения на данный час тоже уже немало», — написало ведомство.
 """
 
+class Request(BaseModel):
+    text: str
+
+
+class Result(BaseModel):
+    sentiment_score: float
+    sentiment_label: str
+    summary: str
+        
+    def to_str(self):
+        return f"Summary:  {self.summary}\n Sentiment:  {self.sentiment_label} ({self.sentiment_score:.3f})"    
+
+# class Response(BaseModel):
+#     results: List[Result] # list of Result objects
+
 class Summarizer():
     ru_summary_pipe: pipeline
     ru_sentiment_pipe: pipeline
@@ -96,21 +112,33 @@ class Summarizer():
                    'ru': self.ru_sentiment_pipe,}
         return summary[lang], sentiment[lang]
         
-    def summarize(self, text: str, lang: str = 'en') -> str:
-        result = {}
-        sum_pipe, sent_pipe = self.get_pipe(lang)
+    # def summarize(self, text: str, lang: str = 'en') -> str:
+    #     result = {}
+    #     sum_pipe, sent_pipe = self.get_pipe(lang)
+        
+    #     response_summary = sum_pipe(text)
+    #     logger.info(response_summary)
+    #     result["summary"] = response_summary[0]["summary_text"]
         
+    #     response_sentiment = sent_pipe(text)
+    #     logger.info(response_sentiment)
+    #     result["sentiment_label"] = response_sentiment[0]["label"]
+    #     result["sentiment_score"] = response_sentiment[0]["score"]
+        
+    #     return f"Summary:  {result['summary']}\n Sentiment:  {result['sentiment_label']} ({result['sentiment_score']:.3f})"    
+
+    def summarize(self, text: Request, lang: str = 'en') -> str:
+        sum_pipe, sent_pipe = self.get_pipe(lang)
         response_summary = sum_pipe(text)
         logger.info(response_summary)
-        result["summary"] = response_summary[0]["summary_text"]
-        
         response_sentiment = sent_pipe(text)
         logger.info(response_sentiment)
-        result["sentiment_label"] = response_sentiment[0]["label"]
-        result["sentiment_score"] = response_sentiment[0]["score"]
-        
-        return f"Summary:  {result['summary']}\n Sentiment:  {result['sentiment_label']} ({result['sentiment_score']:.3f})"    
-
+        result =  Result(
+            summary=response_summary[0]["summary_text"],
+            sentiment_label=response_sentiment[0]["label"],
+            sentiment_score=response_sentiment[0]["score"],
+        )
+        return result
 
 if __name__ == "__main__":
     pipe = Summarizer()
@@ -133,12 +161,12 @@ if __name__ == "__main__":
                 ru_inbtn = gr.Button("Запустить")
                 
         en_inbtn.click(
-            pipe.summarize,
+            pipe.summarize.to_str(),
             [en_inputs, en_lang],
             [en_outputs],
         )
         ru_inbtn.click(
-            pipe.summarize,
+            pipe.summarize.to_str(),
             [ru_inputs, ru_lang],
             [ru_outputs],
         )

main.py

@@ -0,0 +1,47 @@
+import gradio as gr
+from fastapi import FastAPI
+from typing import List
+from app import Summarizer, Request, Result
+from app import EN_SENTIMENT_MODEL, EN_SUMMARY_MODEL, RU_SENTIMENT_MODEL, RU_SUMMARY_MODEL
+from app import DEFAULT_EN_TEXT, DEFAULT_RU_TEXT
+
+app = FastAPI()
+pipe = Summarizer()
+
+
+@app.post("/summ_ru", response_model=Result)
+async def ru_summ_api(request: Request):
+    results = pipe.summarize(request.text)
+    return results
+    
+    
+if __name__ == "__main__":
+
+    with gr.Blocks() as demo:
+        with gr.Row():
+            with gr.Column(scale=2, min_width=600):
+                en_sum_description=gr.Markdown(value=f"Model for Summary: {EN_SUMMARY_MODEL}")
+                en_sent_description=gr.Markdown(value=f"Model for Sentiment: {EN_SENTIMENT_MODEL}")
+                en_inputs=gr.Textbox(label="en_input", lines=5, value=DEFAULT_EN_TEXT, placeholder=DEFAULT_EN_TEXT)
+                en_lang=gr.Textbox(value='en',visible=False)
+                en_outputs=gr.Textbox(label="en_output", lines=5, placeholder="Summary and Sentiment would be here...")
+                en_inbtn = gr.Button("Proceed")
+            with gr.Column(scale=2, min_width=600):
+                ru_sum_description=gr.Markdown(value=f"Model for Summary: {RU_SUMMARY_MODEL}")
+                ru_sent_description=gr.Markdown(value=f"Model for Sentiment: {RU_SENTIMENT_MODEL}")
+                ru_inputs=gr.Textbox(label="ru_input", lines=5, value=DEFAULT_RU_TEXT, placeholder=DEFAULT_RU_TEXT)
+                ru_lang=gr.Textbox(value='ru',visible=False)
+                ru_outputs=gr.Textbox(label="ru_output", lines=5, placeholder="Здесь будет обобщение и эмоциональный окрас текста...")
+                ru_inbtn = gr.Button("Запустить")
+                
+        en_inbtn.click(
+            pipe.summarize.to_str(),
+            [en_inputs, en_lang],
+            [en_outputs],
+        )
+        ru_inbtn.click(
+            pipe.summarize.to_str(),
+            [ru_inputs, ru_lang],
+            [ru_outputs],
+        )
+    demo.launch(show_api=False)   

poetry.lock

@@ -327,20 +327,20 @@ test = ["pytest (>=6)"]
 
 [[package]]
 name = "fastapi"
-version = "0.103.0"
+version = "0.109.0"
 description = "FastAPI framework, high performance, easy to learn, fast to code, ready for production"
 category = "main"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "fastapi-0.103.0-py3-none-any.whl", hash = "sha256:61ab72c6c281205dd0cbaccf503e829a37e0be108d965ac223779a8479243665"},
-    {file = "fastapi-0.103.0.tar.gz", hash = "sha256:4166732f5ddf61c33e9fa4664f73780872511e0598d4d5434b1816dc1e6d9421"},
+    {file = "fastapi-0.109.0-py3-none-any.whl", hash = "sha256:8c77515984cd8e8cfeb58364f8cc7a28f0692088475e2614f7bf03275eba9093"},
+    {file = "fastapi-0.109.0.tar.gz", hash = "sha256:b978095b9ee01a5cf49b19f4bc1ac9b8ca83aa076e770ef8fd9af09a2b88d191"},
 ]
 
 [package.dependencies]
 pydantic = ">=1.7.4,<1.8 || >1.8,<1.8.1 || >1.8.1,<2.0.0 || >2.0.0,<2.0.1 || >2.0.1,<2.1.0 || >2.1.0,<3.0.0"
-starlette = ">=0.27.0,<0.28.0"
-typing-extensions = ">=4.5.0"
+starlette = ">=0.35.0,<0.36.0"
+typing-extensions = ">=4.8.0"
 
 [package.extras]
 all = ["email-validator (>=2.0.0)", "httpx (>=0.23.0)", "itsdangerous (>=1.1.0)", "jinja2 (>=2.11.2)", "orjson (>=3.2.1)", "pydantic-extra-types (>=2.0.0)", "pydantic-settings (>=2.0.0)", "python-multipart (>=0.0.5)", "pyyaml (>=5.3.1)", "ujson (>=4.0.1,!=4.0.2,!=4.1.0,!=4.2.0,!=4.3.0,!=5.0.0,!=5.1.0)", "uvicorn[standard] (>=0.12.0)"]
@@ -2158,14 +2158,14 @@ files = [
 
 [[package]]
 name = "starlette"
-version = "0.27.0"
+version = "0.35.1"
 description = "The little ASGI library that shines."
 category = "main"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "starlette-0.27.0-py3-none-any.whl", hash = "sha256:918416370e846586541235ccd38a474c08b80443ed31c578a418e2209b3eef91"},
-    {file = "starlette-0.27.0.tar.gz", hash = "sha256:6a6b0d042acb8d469a01eba54e9cda6cbd24ac602c4cd016723117d6a7e73b75"},
+    {file = "starlette-0.35.1-py3-none-any.whl", hash = "sha256:50bbbda9baa098e361f398fda0928062abbaf1f54f4fadcbe17c092a01eb9a25"},
+    {file = "starlette-0.35.1.tar.gz", hash = "sha256:3e2639dac3520e4f58734ed22553f950d3f3cb1001cd2eaac4d57e8cdc5f66bc"},
 ]
 
 [package.dependencies]
@@ -2552,14 +2552,14 @@ zstd = ["zstandard (>=0.18.0)"]
 
 [[package]]
 name = "uvicorn"
-version = "0.25.0"
+version = "0.27.0"
 description = "The lightning-fast ASGI server."
 category = "main"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "uvicorn-0.25.0-py3-none-any.whl", hash = "sha256:ce107f5d9bd02b4636001a77a4e74aab5e1e2b146868ebbad565237145af444c"},
-    {file = "uvicorn-0.25.0.tar.gz", hash = "sha256:6dddbad1d7ee0f5140aba5ec138ddc9612c5109399903828b4874c9937f009c2"},
+    {file = "uvicorn-0.27.0-py3-none-any.whl", hash = "sha256:890b00f6c537d58695d3bb1f28e23db9d9e7a17cbcc76d7457c499935f933e24"},
+    {file = "uvicorn-0.27.0.tar.gz", hash = "sha256:c855578045d45625fd027367f7653d249f7c49f9361ba15cf9624186b26b8eb6"},
 ]
 
 [package.dependencies]
@@ -2683,4 +2683,4 @@ dev = ["black (>=19.3b0)", "pytest (>=4.6.2)"]
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10"
-content-hash = "baa66896f43ca309c23f1dc2f6e6902a89e532d449410ecaa4bed5ddeca83a7b"
+content-hash = "a0a8f5d7bbfd3d08c83e048ee2f8e18cf31729338afa2484b5f817c387457622"

pyproject.toml

@@ -16,6 +16,8 @@ pretrainedmodels = "^0.7.4"
 sentencepiece = "^0.1.99"
 protobuf = "^4.25.1"
 loguru = "^0.7.2"
+fastapi = "^0.109.0"
+uvicorn = "^0.27.0"
 
 
 [tool.poetry.group.dev.dependencies]