Update app.py

app.py

@@ -1,10 +1,6 @@
 import gradio as gr
 import torch
-from transformers import (
-    AutoTokenizer,
-    AutoModelForSequenceClassification,
-    pipeline,
-)
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
 
 # =====================
 # DEVICE
@@ -19,7 +15,6 @@ def clamp(x: float, lo: float = -5.0, hi: float = 5.0) -> float:
 
 def score01_to_minus5_plus5(p: float) -> float:
     """
-    Перевод вероятности 0..1 в шкалу -5..+5:
     0.0 -> -5
     0.5 -> 0
     1.0 -> +5
@@ -43,8 +38,8 @@ def load_mnli():
 
 def agreement_score_minus5_plus5(msg1: str, msg2: str) -> float:
     """
-    Agreement: -5 = противоречие, +5 = согласие
-    Берём entailment - contradiction => [-1..+1], потом *5 => [-5..+5]
+    -5 = contradiction
+    +5 = entailment
     """
     load_mnli()
     inputs = mnli_tokenizer(msg1, msg2, return_tensors="pt", truncation=True).to(DEVICE)
@@ -52,7 +47,8 @@ def agreement_score_minus5_plus5(msg1: str, msg2: str) -> float:
         logits = mnli_model(**inputs).logits
     probs = torch.softmax(logits, dim=-1)[0]
 
-    raw = (probs[2] - probs[0]).item()  # [-1..+1]
+    # entailment - contradiction => [-1..+1]
+    raw = (probs[2] - probs[0]).item()
     return round(clamp(raw * 5), 2)
 
 # =====================
@@ -79,7 +75,7 @@ def analyze_sentiment(text: str) -> float:
     with torch.no_grad():
         logits = sent_model(**inputs).logits
     probs = torch.softmax(logits, dim=-1)
-    stars = torch.argmax(probs, dim=-1).item() + 1  # 1..5
+    stars = torch.argmax(probs, dim=-1).item() + 1
     score = (stars - 3) * 2.5
     return round(clamp(score), 2)
 
@@ -96,18 +92,18 @@ def load_sarcasm():
             "text-classification",
             model=SARCASM_MODEL,
             device=0 if torch.cuda.is_available() else -1,
-            truncation=True
+            truncation=True,
         )
 
 def sarcasm_score(text: str) -> float:
     """
-    +5 = очень вероятно сарказм/ирония
-    -5 = очень вероятно НЕ сарказм
+    +5 = irony
+    -5 = non-irony
     """
     load_sarcasm()
     res = sarcasm_pipe(text)[0]
     label = res["label"].lower()
-    conf = float(res["score"])  # 0..1
+    conf = float(res["score"])
 
     if "irony" in label:
         return round(clamp(conf * 5), 2)
@@ -116,15 +112,14 @@ def sarcasm_score(text: str) -> float:
 # =====================
 # 4) Neutrality -> [-5..+5]
 # =====================
-# +5 = максимально нейтрально
-# -5 = максимально заряжено/эмоционально/полярно
-#
-# Простая логика:
-# neutrality = 5 - (|sentiment| + max(0, sarcasm))/2
-# (сарказм делает текст менее нейтральным)
 def neutrality_score(text: str) -> float:
-    sent = abs(analyze_sentiment(text))          # 0..5
-    sarc = max(0.0, sarcasm_score(text))         # 0..5 (только если сарказм есть)
+    """
+    +5 = максимально нейтрально
+    -5 = максимально эмоционально/заряжено
+    """
+    sent = abs(analyze_sentiment(text))     # 0..5
+    sarc = max(0.0, sarcasm_score(text))    # 0..5 (только если irony)
+
     neutrality = 5.0 - (sent + sarc) / 2.0
     return round(clamp(neutrality), 2)
 
@@ -132,24 +127,22 @@ def neutrality_score(text: str) -> float:
 # 5) Agreement with irony adjustment
 # =====================
 def agreement_with_irony(msg1: str, msg2: str) -> float:
-    """
-    Ирония снижает "уверенность" agreement.
-    """
     base = agreement_score_minus5_plus5(msg1, msg2)
+
     s2 = max(0.0, sarcasm_score(msg2))  # 0..5
     sarcasm_strength = s2 / 5.0         # 0..1
 
+    # чем больше сарказм, тем меньше доверяем agreement
     multiplier = 1.0 - 0.65 * sarcasm_strength
     final_score = base * multiplier
     return round(clamp(final_score), 2)
 
 # =====================
-# 6) Zero-Shot Multilabel Classification -> [-5..+5]
+# 6) Multilabel Zero-Shot -> [-5..+5]
 # =====================
 ZS_MODEL = "facebook/bart-large-mnli"
 zs_classifier = None
 
-# Твои категории + расширение под Twitter/X дискуссии
 CATEGORIES = [
     # базовые
     "politique",
@@ -192,14 +185,10 @@ def load_zero_shot():
         zs_classifier = pipeline(
             "zero-shot-classification",
             model=ZS_MODEL,
-            device=0 if torch.cuda.is_available() else -1
+            device=0 if torch.cuda.is_available() else -1,
         )
 
 def classify_message(text: str) -> dict:
-    """
-    Возвращает словарь {label: rating} где rating в [-5..+5]
-    Важно: это не "истина", а "уверенность модели" относительно метки.
-    """
     load_zero_shot()
     result = zs_classifier(text, candidate_labels=CATEGORIES, multi_label=True)
 
@@ -223,11 +212,10 @@ with gr.Blocks(title="Unified NLP API (-5..+5)") as demo:
 - **Sentiment**: -5 = негатив, +5 = позитив  
 - **Sarcasm**: -5 = уверенно НЕ сарказм, +5 = уверенно сарказм/ирония  
 - **Neutrality**: +5 = максимально нейтрально, -5 = максимально “заряжено”  
-- **Multilabel**: рейтинг уверенности метки в шкале -5..+5 (0.5 → 0)
+- **Multilabel**: уверенность метки в шкале -5..+5 (0.5 → 0)
         """
     )
 
-    # Agreement
     with gr.Tab("Agreement"):
         msg1 = gr.Textbox(label="Message 1")
         msg2 = gr.Textbox(label="Message 2")
@@ -241,30 +229,28 @@ with gr.Blocks(title="Unified NLP API (-5..+5)") as demo:
         out_agree_irony = gr.Number(label="Agreement Score (irony-aware) (-5..+5)")
         btn_agree_irony.click(fn=agreement_with_irony, inputs=[msg1, msg2], outputs=out_agree_irony)
 
-    # Sentiment
     with gr.Tab("Sentiment"):
         text_sent = gr.Textbox(label="Text")
         btn_sent = gr.Button("Analyze Sentiment")
         out_sent = gr.Number(label="Sentiment Score (-5..+5)")
         btn_sent.click(fn=analyze_sentiment, inputs=text_sent, outputs=out_sent)
 
-    # Sarcasm
     with gr.Tab("Sarcasm / Irony"):
         text_sarc = gr.Textbox(label="Text")
         btn_sarc = gr.Button("Analyze Sarcasm")
         out_sarc = gr.Number(label="Sarcasm Score (-5..+5)")
         btn_sarc.click(fn=sarcasm_score, inputs=text_sarc, outputs=out_sarc)
 
-    # Neutrality
     with gr.Tab("Neutrality"):
         text_neu = gr.Textbox(label="Text")
         btn_neu = gr.Button("Analyze Neutrality")
         out_neu = gr.Number(label="Neutrality Score (-5..+5)")
         btn_neu.click(fn=neutrality_score, inputs=text_neu, outputs=out_neu)
 
-    # Multilabel
     with gr.Tab("Multilabel Classification"):
         text_clf = gr.Textbox(label="Text")
         btn_clf = gr.Button("Classify")
         out_clf = gr.Label(label="Categories & Scores (-5..+5)")
-        btn_clf.click(fn=classify_message, inputs=text_clf, outputs=out_cl
+        btn_clf.click(fn=classify_message, inputs=text_clf, outputs=out_clf)
+
+demo.launch()