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from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from sentence_transformers import SentenceTransformer,util
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
import uvicorn
import numpy as np
import pandas as pd
# Initialize the FastAPI app
app = FastAPI()
# Load the pre-trained SentenceTransformer model
model = SentenceTransformer("Alibaba-NLP/gte-base-en-v1.5", trust_remote_code=True)
# Define the request body schema
class TextInput(BaseModel):
text: str
# Home route
@app.get("/")
async def home():
return {"message": "welcome to home page"}
# Define the API endpoint for generating embeddings
@app.post("/embed")
async def generate_embedding(text_input: TextInput):
"""
Generate a 768-dimensional embedding for the input text.
Returns the embedding in a structured format with rounded values.
"""
try:
# Generate the embedding
embedding = model.encode(text_input.text, convert_to_tensor=True).cpu().numpy()
# Round embedding values to 2 decimal places
rounded_embedding = np.round(embedding, 2).tolist()
# Return structured response
return {
"dimensions": len(rounded_embedding),
"embeddings": [rounded_embedding]
}
except Exception as e:
# Handle any errors
raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")
# Load pre-trained SentenceTransformer model
model = SentenceTransformer("Alibaba-NLP/gte-base-en-v1.5", trust_remote_code=True)
# Train the Logistic Regression model during app startup
df = pd.read_excel("sms_process_data_main.xlsx")
X_train, X_test, y_train, y_test = train_test_split(df["MessageText"], df["label"], test_size=0.2, random_state=42)
X_train_embeddings = model.encode(X_train.tolist())
# Initialize and train the Logistic Regression model
logreg_model = LogisticRegression(max_iter=100)
logreg_model.fit(X_train_embeddings, y_train)
# Define input schema
class TextInput(BaseModel):
text: str
@app.post("/prediction")
async def generate_prediction(text_input: TextInput):
"""
Predict the label for the given text input using the trained model.
"""
try:
# Generate embedding for the input text
new_embedding = model.encode([text_input.text])
# Predict the label using the trained Logistic Regression model
prediction = logreg_model.predict(new_embedding).tolist()[0] # Extract single prediction
# Return structured response
return {
"predicted_label": prediction
}
except Exception as e:
# Handle any errors
raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")
class SentencesInput(BaseModel):
sentence1: str
sentence2: str
@app.post("/text_to_tensor")
def text_to_tensor(input: SentencesInput):
try:
# Generate embeddings
embeddings = model.encode([input.sentence1, input.sentence2])
# Compute cosine similarity
cosine_similarity = util.cos_sim(embeddings[0], embeddings[1]).item()
return {"cosine_similarity": round(cosine_similarity, 3)}
except Exception as e:
raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")
if __name__ == "__main__":
uvicorn.run(app, host="0.0.0.0", port=7860)
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