Update main.py

main.py

@@ -44,47 +44,46 @@ async def receive_updates(request: Request):
     print(f"Original Message ID: {message_id}")
     print(f"User ID: {user_id}")
     print(f"Chat ID: {chat_id}")
-# async def receive_message(message: Message):
-#     print("Received message:", message.message)
-#     influent_paragraph = message.message
-#     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"}
+
+    influent_paragraph = user_message
+    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"}