Create app.py

app.py

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+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)