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@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+prod_models/emo_modelV2_tf/variables/variables.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text
+prod_models/emo_modelV2.keras filter=lfs diff=lfs merge=lfs -text

api.py

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+# Built-in
+import re
+import joblib
+from pathlib import Path
+import os
+import uvicorn
+
+# Dependencies for FastAPI
+from fastapi import FastAPI
+from fastapi.responses import RedirectResponse
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+
+import keras
+import tensorflow as tf
+
+# Set Environment
+os.environ["KERAS_BACKEND"] = "tensorflow"
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1' 
+
+# Setup Paths
+lr_model_path = Path('./prod_models/emotion_classifier_pipe_lr.pkl')
+keras_model_path = Path('./prod_models/emo_modelV2.keras')
+
+# Class for Text Body
+class Paragraph(BaseModel):
+    input: str
+
+# Classes 
+classes = ['admiration', 'amusement', 'anger', 'annoyance', 'approval', 'caring', 'confusion', 'curiosity', 'desire', 'disappointment', 'disapproval', 'disgust', 'embarrassment', 'excitement', 'fear', 'gratitude', 'grief', 'joy', 'love', 'nervousness', 'optimism', 'pride', 'realization', 'relief', 'remorse', 'sadness', 'surprise', 'neutral']
+
+# Load the Logistic Regression Model
+with open(lr_model_path, 'rb') as f:
+    lr_model = joblib.load(f)
+
+# Load the Keras Model
+keras_model = keras.models.load_model(str(keras_model_path), compile=True)
+
+# Start the app
+app = FastAPI()
+
+# Setup CORS policy
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# APIs
+@app.get("/")
+async def welcome():
+    return {"message":"Hello, this is the API server for Sentimetry. Go to /docs to test the APIs."}
+
+@app.get("/d")
+async def docs():
+    # return {"message":"Hello, this is the API server for Sentimetry. Go to /docs to test the APIs."} 
+    return RedirectResponse(url="/docs")
+
+@app.post("/logistic-regression")
+async def predict_emotions_lr(paragraph : Paragraph):
+    # Split the huge chunk of text into a list of strings
+    text_list = [text.strip() for text in re.split(r'[.!?;\n]', paragraph.input) if text.strip()]
+
+    # Create a list to store predictions per text
+    predictions_per_text = []
+    for text in text_list:
+      emotion = [{'label': label, 'score': score} for label, score in zip(lr_model.classes_, lr_model.predict_proba([text])[0])]
+      predictions_per_text.append(emotion)
+
+    # Create a dictionary to aggregate scores for each label
+    total = {}
+
+    # Iterate over each list and aggregate the scores
+    for prediction in predictions_per_text:
+        for emotion_dict in prediction:
+            label = emotion_dict['label']
+            score = emotion_dict['score']
+            total[label] = total.get(label, 0) + score
+
+    # Convert the dictionary to a list of dictionaries
+    result = [{"label": label, "score": score} for label, score in total.items()]
+
+    # Sort the result in descending order based on score
+    sorted_result = sorted(result, key=lambda x: x['score'], reverse=True)
+
+    return {"predictions": sorted_result}
+
+@app.post("/keras")
+async def predict_emotions_keras(paragraph : Paragraph):
+    # Split the huge chunk of text into a list of strings
+    text_list = [text.strip() for text in re.split(r'[.!?;\n]', paragraph.input) if text.strip()]
+
+    # Create a list to store predictions per text
+    predictions_per_text = []
+    for text in text_list:
+        scores = keras_model(tf.constant([text]))['dense_1'][0]
+        emotion = [{'label': label, 'score': score} for label, score in zip(classes, scores.numpy())]
+        print(emotion)
+        predictions_per_text.append(emotion)
+
+    # Create a dictionary to aggregate scores for each label
+    total = {}
+
+    # Iterate over each list and aggregate the scores
+    for prediction in predictions_per_text:
+        for emotion_dict in prediction:
+            label = emotion_dict['label']
+            score = emotion_dict['score']
+            total[label] = total.get(label, 0) + score
+
+    # Convert the dictionary to a list of dictionaries
+    result = [{"label": label, "score": score} for label, score in total.items()]
+
+    # Sort the result in descending order based on score
+    sorted_result = sorted(result, key=lambda x: x['score'], reverse=True)
+
+    return {"predictions": sorted_result}
+
+if __name__ == "__main__":
+  uvicorn.run("api:app", host="0.0.0.0", port=8000, reload=True)

prod_models/emo_modelV2.keras

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prod_models/emo_modelV2_tf/fingerprint.pb

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prod_models/emo_modelV2_tf/keras_metadata.pb

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prod_models/emo_modelV2_tf/saved_model.pb

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prod_models/emo_modelV2_tf/variables/variables.data-00000-of-00001

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prod_models/emotion_classifier_pipe_lr.pkl

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requirements.txt

@@ -0,0 +1,18 @@
+absl-py>=2.1.0
+fastapi>=0.110.0
+joblib>=1.3.2
+keras>=3.0.5
+matplotlib>=3.8.3
+numpy>=1.26.4
+pandas>=2.2.1
+protobuf>=4.25.3
+pydantic>=2.6.4
+pydantic_core>=2.16.3
+retvec>=1.0.1
+scikit-learn>=1.4.1.post1
+scipy>=1.12.0
+six>=1.16.0
+starlette>=0.36.3
+tensorboard>=2.16.2
+tensorflow>=2.16.1
+uvicorn>=0.28.0