working version

main.py

@@ -1,33 +0,0 @@
-from typing import Union
-from src.utils import make_incredible_predictions
-from fastapi import FastAPI
-
-app = FastAPI()
-# /docs, page to see auto-generated API documentation
-
-@app.get("/")
-def read_root():
-    return {"Hello": "World", "cohort": "2"}
-
-
-@app.get("/items/{item_id}")
-def read_item(item_id: int, q: Union[str, None] = None):
-    return {"item_id": item_id, "q": q}
-
-@app.get("/predict")
-def predict(age, salary, dependentsNumber, gender):
-    prediction = None
-    # prediction = model.predict(pd.DataFrame([age, salary, dependents_number, gender]))
-    return {"age":age, 
-            "salary":salary, 
-            "dependents_number":dependentsNumber, 
-            "gender":gender,"prediction":prediction}
-
-@app.post("/predict")
-def predict(age, salary, dependentsNumber, gender):
-    prediction = None
-    # prediction = model.predict(pd.DataFrame([age, salary, dependents_number, gender]))
-    return {"age":age, 
-            "salary":salary, 
-            "dependents_number":dependentsNumber, 
-            "gender":gender,"prediction":prediction}

main_sentiment.py

@@ -1,58 +0,0 @@
-# Imports
-import os
-from typing import Union
-from src.utils import preprocess
-from fastapi import FastAPI
-from transformers import AutoModelForSequenceClassification,AutoTokenizer, AutoConfig
-import numpy as np
-#convert logits to probabilities
-from scipy.special import softmax
-
-# Config
-
-app = FastAPI()
-#/docs, page to see auto-generated API documentation
-
-#loading ML/DL components
-os.environ['SENTENCE_TRANSFORMERS_HOME'] = './.cache'
-tokenizer = AutoTokenizer.from_pretrained('bert-base-cased')
-model_path = f"Junr-syl/tweet_sentiments_analysis"
-config = AutoConfig.from_pretrained(model_path)
-config.id2label = {0: 'NEGATIVE', 1: 'NEUTRAL', 2: 'POSITIVE'}
-model = AutoModelForSequenceClassification.from_pretrained(model_path)
-
-# Endpoints
-@app.get("/")
-def read_root():
-    "Home endpoint"
-    return {"greeting": "Hello World..!", 
-            "cohort": "2",
-            }
-
-@app.post("/predict")
-def predict(text:str):
-    "prediction endpoint, classifying tweets"
-    try:
-        text = preprocess(text)
-
-        # PyTorch-based models
-        encoded_input = tokenizer(text, return_tensors='pt')
-        output = model(**encoded_input)
-        scores = output[0][0].detach().numpy()
-        scores = softmax(scores)
-
-        #Process scores
-        ranking = np.argsort(scores)
-        ranking = ranking[::-1]  
-        predicted_label = config.id2label[ranking[0]]
-        predicted_score = scores[ranking[0]]
-
-
-        return {"text":text,
-                "predicted_label":predicted_label,
-                "confidence_score":predicted_score
-                }
-    except Exception as e:
-        return {
-            "error": e
-        }

src/main_sentiment.py

@@ -1,10 +1,5 @@
 # Imports
-import sys
-# sys.path.insert(0, '../src/')
-# sys.path.insert(0, '../src')
-# sys.path.insert(0, 'src/')
-# sys.path.insert(0, 'src')
-
+import os
 from typing import Union
 from src.utils import preprocess
 from fastapi import FastAPI
@@ -19,6 +14,7 @@ app = FastAPI()
 #/docs, page to see auto-generated API documentation
 
 #loading ML/DL components
+os.environ['SENTENCE_TRANSFORMERS_HOME'] = './.cache'
 tokenizer = AutoTokenizer.from_pretrained('bert-base-cased')
 model_path = f"Junr-syl/tweet_sentiments_analysis"
 config = AutoConfig.from_pretrained(model_path)
@@ -36,23 +32,35 @@ def read_root():
 @app.post("/predict")
 def predict(text:str):
     "prediction endpoint, classifying tweets"
-    
-    text = preprocess(text)
+    print(f"\n[Info] Starting prediction")
+    try:
+        text = preprocess(text)
 
-    # PyTorch-based models
-    encoded_input = tokenizer(text, return_tensors='pt')
-    output = model(**encoded_input)
-    scores = output[0][0].detach().numpy()
-    scores = softmax(scores)
+        # PyTorch-based models
+        encoded_input = tokenizer(text, return_tensors='pt')
+        output = model(**encoded_input)
+        scores = output[0][0].detach().numpy()
+        scores = softmax(scores)
 
-    #Process scores
-    ranking = np.argsort(scores)
-    ranking = ranking[::-1]  
-    predicted_label = config.id2label[ranking[0]]
-    predicted_score = scores[ranking[0]]
+        #Process scores
+        ranking = np.argsort(scores)
+        ranking = ranking[::-1]  
+        predicted_label = config.id2label[ranking[0]]
+        predicted_score = float(scores[ranking[0]])
 
-
-    return {"text":text,
-            "predicted_label":predicted_label,
-            "confidence_score":predicted_score
-            }
+        
+        response = {"text":text,
+                "predicted_label":predicted_label,
+                "confidence_score":predicted_score
+                }
+        
+        print(f"\n[Info] Prediction done.")
+        print(f"\n[Info] Have a look at the API response")
+        print(response)
+        
+        return response
+    
+    except Exception as e:
+        return {
+            "error": str(e)
+        }