app.py

from fastapi import FastAPI
from pydantic import BaseModel
import torch
import numpy as np
from transformers import AutoTokenizer, AutoModel
from sklearn.linear_model import LogisticRegression
import uvicorn

app = FastAPI()

# Load Hugging Face model
model_name = "bert-base-uncased"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

# Function to get text embeddings
def get_embedding(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
    with torch.no_grad():
        outputs = model(**inputs)
    return outputs.last_hidden_state[:, 0, :].numpy()

# Sample dataset
texts = ["I love this!", "This is terrible.", "Fantastic experience!", "I hate it.", "Absolutely wonderful!", "Worst ever!"]
labels = [1, 0, 1, 0, 1, 0]  # 1 = Positive, 0 = Negative
X = np.vstack([get_embedding(text) for text in texts])
y = np.array(labels)

# Train model
clf = LogisticRegression()
clf.fit(X, y)

# Define request format
class InputText(BaseModel):
    text: str

@app.post("/predict")
def predict_sentiment(data: InputText):
    user_embedding = get_embedding(data.text)
    prediction = clf.predict(user_embedding)
    sentiment = "Positive 😊" if prediction[0] == 1 else "Negative 😡"
    return {"sentiment": sentiment}

if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=7860)