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f118d14 4aa41c7 f118d14 8cd5417 f118d14 8cd5417 f118d14 8cd5417 f118d14 4aa41c7 f118d14 4aa41c7 f118d14 4aa41c7 f118d14 8cd5417 f118d14 4aa41c7 f118d14 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 | from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from transformers import pipeline
from typing import List
app = FastAPI(title="NER + Emotion API")
# ---------------------------------------------------------
# LOAD NER FIRST (PRIORITY LOAD)
# ---------------------------------------------------------
print("Loading NER model...")
ner_pipeline = pipeline(
"ner",
model="dslim/bert-base-NER",
aggregation_strategy="simple"
)
print("NER model loaded.")
# ---------------------------------------------------------
# LOAD SENTIMENT SECOND
# ---------------------------------------------------------
print("Loading Sentiment model...")
sentiment_pipeline = pipeline(
"text-classification",
model="j-hartmann/emotion-english-distilroberta-base",
top_k=1
)
print("Sentiment model loaded.")
# ---------------------------------------------------------
# REQUEST MODELS
# ---------------------------------------------------------
class TextInput(BaseModel):
text: str
class SentimentInput(BaseModel):
sentences: List[str]
# ---------------------------------------------------------
# HEALTH CHECK
# ---------------------------------------------------------
@app.get("/")
def home():
return {"message": "NER + Emotion API is running"}
# ---------------------------------------------------------
# NER ENDPOINT
# ---------------------------------------------------------
# ---------------------------------------------------------
# NER ENDPOINT (UPDATED)
# ---------------------------------------------------------
@app.post("/analyze/ner")
def analyze_ner(data: TextInput):
try:
# REMOVED truncation=True to fix the 500 error
results = ner_pipeline(data.text, aggregation_strategy="simple")
persons = []
locations = []
organizations = []
for entity in results:
label = entity["entity_group"]
word = entity["word"].strip()
# dslim/bert-base-NER uses these labels:
if label == "PER":
persons.append(word)
elif label == "LOC":
locations.append(word)
elif label == "ORG":
organizations.append(word)
return {
"persons": list(set(persons)),
"locations": list(set(locations)),
"organizations": list(set(organizations))
}
except Exception as e:
# This will help you see the exact error in HF logs
print(f"Internal Error: {str(e)}")
raise HTTPException(status_code=500, detail=str(e))
# ---------------------------------------------------------
# SENTIMENT ENDPOINT
# ---------------------------------------------------------
@app.post("/analyze/sentiment")
def analyze_sentiment(data: SentimentInput):
try:
results = sentiment_pipeline(
data.sentences,
truncation=True,
max_length=512
)
processed_results = []
for res_list in results:
top_result = res_list[0]
label = top_result["label"]
score = top_result["score"]
if label == "joy":
polarity = score
elif label in ["anger", "disgust", "fear", "sadness"]:
polarity = -score
else:
polarity = 0.0
processed_results.append({
"label": label,
"confidence": score,
"polarity": polarity
})
return {"results": processed_results}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=7860)
|