Yesss

.gitattributes

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README.md

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----
-title: Emotion Detector
-emoji: 🚀
-colorFrom: pink
-colorTo: pink
-sdk: gradio
-sdk_version: 5.31.0
-app_file: app.py
-pinned: false
-short_description: AI emotion detector
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Emotion Analysis Api
+emoji: 🚀
+colorFrom: pink
+colorTo: pink
+sdk: gradio
+sdk_version: 5.31.0
+app_file: app.py
+model :  model.py
+pinned: false
+short_description: AI emotion detector
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

main.py

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+#Emotion Detection API using FastAPI
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from app.model import predict_emotions, calculate_distress
+
+app = FastAPI()
+
+class TextInput(BaseModel):
+    text: str
+
+@app.post("/analyze")
+async def analyze(input: TextInput):
+    try:
+        emotions = predict_emotions(input.text)
+        distress_score = calculate_distress(emotions)
+
+        return {
+            "text": input.text,
+            "emotions": emotions,
+            "distress_score": distress_score,
+            "warning": distress_score > 1.5  # Trigger warning if score high
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+@app.get("/")
+def root():
+    return {"message": "Emotion detector is live"}

model.py

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+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+import numpy as np
+
+# Load model and tokenizer
+model_name = "SamLowe/roberta-base-go_emotions"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+#Define distress weights for emotions 
+DISTRESS_WEIGHTS = {
+    "grief": 1.0,
+    "sadness": 0.9,
+    "fear": 0.9,
+    "disgust": 0.9,
+    "anger": 0.9,
+    "remorse": 0.8,
+    "disappointment": 0.8,
+    "nervousness": 0.8,
+    "disapproval": 0.7,
+    "embarrassment": 0.7,
+    "annoyance": 0.6,
+    "confusion": 0.6,
+    "surprise": 0.4,
+    "desire": 0.4,
+    "love": 0.3,
+    "excitement": 0.3,
+    "pride": 0.3,
+    "optimism": 0.3,
+    "admiration": 0.2,
+    "gratitude": 0.2,
+    "relief": 0.2,
+    "joy": 0.2,
+    "amusement": 0.2,
+    "neutral": 0.1,
+}
+# Labels from GoEmotions
+EMOTION_LABELS = tokenizer.convert_ids_to_tokens(list(range(model.config.num_labels)))
+
+# Ensure the model is in evaluation mode
+model.eval()    
+
+def predict_emotions(text: str):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True)
+    outputs = model(**inputs)
+    probs = torch.sigmoid(outputs.logits)[0].detach().numpy()
+
+    threshold = 0.3  # You can tune this
+    predicted = {label: float(prob) for label, prob in zip(model.config.id2label.values(), probs) if prob > threshold}
+    return predicted
+
+def calculate_distress(emotions: dict):
+    distress_score = sum(
+        emotions.get(emotion, 0) * DISTRESS_WEIGHTS.get(emotion, 0)
+        for emotion in emotions
+    )
+    return round(distress_score, 3)

requirements.txt

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-transformers==4.41.0
-fastapi==0.110.2
-uvicorn==0.29.0
-torch>=2.0.0
+transformers==4.41.0
+fastapi==0.110.2
+uvicorn==0.29.0
+torch>=2.0.0

test_payload.json

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+{
+  "text": "I’m not sure how long I can keep doing this. I feel completely overwhelmed and exhausted."
+}