new pipeline

app.py

@@ -1,74 +1,14 @@
 import gradio as gr
 from transformers import pipeline
-import math
+import torch
+import warnings
 
-print("🎭 Loading sentiment analysis pipeline...")
-sentiment_analyzer = pipeline(
-    "sentiment-analysis",
-    model="cardiffnlp/twitter-roberta-base-sentiment-latest",
-    tokenizer="cardiffnlp/twitter-roberta-base-sentiment-latest",
-    return_all_scores=True,
-    truncation=True,
-    padding=True,
-    device_map="auto"
-)
+ner_pipe = pipeline("ner", model="NbAiLab/nb-bert-base-ner", aggregation_strategy="simple")
 
-def _polarity_from_scores(scores):
-    # scores is a list like [{'label': 'negative', 'score': 0.01}, {'label':'neutral',...}, {'label':'positive',...}]
-    probs = {s["label"].lower(): float(s["score"]) for s in scores}
-    p_pos = probs.get("positive", 0.0)
-    p_neg = probs.get("negative", 0.0)
-    return p_pos - p_neg  # range roughly [-1, 1]
+def ner(text):
+  return ner_pipe(text)
 
-def analyze_tone_and_bias(text, chunk_size=500, neutral_margin=0.1):
-    """
-    Analyze emotional tone and potential bias in sources.
-    Uses polarity = P(positive) - P(negative) and respects neutral.
-    """
-    # Make chunks
-    chunks = [text[i:i+chunk_size] for i in range(0, len(text), chunk_size)]
-    chunks = [c for c in chunks if len(c.strip()) > 10]
-    if not chunks:
-        return {"error": "Could not analyze sentiment"}
-
-    # Batch inference
-    batch_outputs = sentiment_analyzer(chunks)  # list of lists of dicts
-
-    # Compute per-chunk polarity
-    chunk_polarities = [_polarity_from_scores(scores) for scores in batch_outputs]
-
-    # Aggregate
-    avg_polarity = sum(chunk_polarities) / len(chunk_polarities)
-    if avg_polarity > neutral_margin:
-        overall = "POSITIVE"
-    elif avg_polarity < -neutral_margin:
-        overall = "NEGATIVE"
-    else:
-        overall = "NEUTRAL"
-
-    # Build a compact per-chunk view
-    # Use the model's top label for human-readable chunk summaries
-    tops = []
-    for scores in batch_outputs[:5]:
-        top = max(scores, key=lambda s: s["score"])
-        tops.append({"label": top["label"].upper(), "score": float(top["score"])})
-
-    return {
-        "overall_sentiment": overall,
-        "confidence": float(abs(avg_polarity)),
-        "chunk_analysis": tops
-    }
-
-print("✅ Sentiment analysis pipeline ready")
-
-def analyze_sentiment(text):
-  result = analyze_tone_and_bias(text)
-  return result['chunk_analysis'][0]
-
-demo = gr.Interface(fn=analyze_sentiment, inputs="textbox", outputs="textbox")
-
-test_txt = "Nærmeste samtlige biler og vogntog som passerte Ørskogfjellet da kontrollen pågikk ble stoppet. Hele 50 personer fra de ulike etatene var involvert i kontrollen."
-
-print(analyze_sentiment(test_txt))
+ner_result = ner("Nærmeste samtlige biler og vogntog som passerte Ørskogfjellet da kontrollen pågikk ble stoppet. Hele 50 personer fra de ulike etatene var involvert i kontrollen.")
 
+demo = gr.Interface(fn=ner, inputs="textbox", outputs="textbox")
 demo.launch(share=True)

requirements.txt

@@ -0,0 +1,4 @@
+transformers
+gradio
+pandas
+torch