Update main.py

main.py

@@ -3,15 +3,76 @@ from pydantic import BaseModel
 from fastapi.responses import RedirectResponse, HTMLResponse
 import urllib.parse
 import requests
+from gramformer import Gramformer
+import torch
+import telebot
+import spacy
+import os
+import concurrent.futures
 
 app = FastAPI()
 
-
 class Message(BaseModel):
     message: str
 
+# Load the spaCy English model
+nlp = spacy.load("en_core_web_sm")
+
+def set_seed(seed):
+    torch.manual_seed(seed)
+    if torch.cuda.is_available():
+        torch.cuda.manual_seed_all(seed)
+
+set_seed(1212)
+
+gf = Gramformer(models=1, use_gpu=False)  # 1=corrector, 2=detector
 
 @app.get("/", response_class=HTMLResponse)
 async def read_root():
         return """Hello Grammar Bot!"""
+
+@app.post("/checkgrammar")
+async def receive_message(message: Message):
+    print("Received message:", message.message)
+    influent_paragraph = message_text
+    corrected_paragraph = influent_paragraph  # Initialize with the input text
+    mistakes_count = 0
+    mistakes = []
+
+    while True:
+        if mistakes_count == 5:
+            break
+        mistakes_count += 1
+        influent_sentences = list(nlp(corrected_paragraph).sents)
+        influent_sentences = [sentence.text for sentence in influent_sentences]
+        print("[Influent sentences]", influent_sentences)
+
+        corrected_paragraph = list(gf.correct(corrected_paragraph, max_candidates=1))[0]
+        print("[Corrected paragraph]", corrected_paragraph)
+
+        new_mistakes = []
+        for influent_sentence in influent_sentences:
+            corrected_sentences = gf.correct(influent_sentence, max_candidates=1)
+            print("[Input]", influent_sentence)
+            for corrected_sentence in corrected_sentences:
+                print("[Correction]", corrected_sentence)
+                new_mistakes += gf.get_edits(influent_sentence, corrected_sentence)
+                print("[Edits]", new_mistakes)
+
+        # If no new mistakes are found, exit the loop
+        if not new_mistakes:
+            break
+
+        # Add the new mistakes to the list
+        mistakes += new_mistakes
+
+    full_output = "Corrected Paragraph:\n" + corrected_paragraph + "\n\nMistakes:\n"
+    for index, (tag, mistake, start, end, correction, corr_start, corr_end) in enumerate(mistakes, start=1):
+        full_output += f"{index}. Tag: {tag}\n"
+        full_output += f"   Mistake: {mistake} (Position: {start}-{end})\n"
+        full_output += f"   Correction: {correction} (Position: {corr_start}-{corr_end})\n"
+        full_output += "\n"
+
+    print(full_output)
+    return {"message": "Message received successfully"}