Upload app.py

app.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+import torch
+from transformers import AutoTokenizer, AutoModel
+import uvicorn
+import gradio as gr
+
+# Initialize FastAPI app
+app = FastAPI()
+
+# Load pre-trained model and tokenizer
+model_name = "bert-base-uncased"  # You can change this to another model
+
+try:
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModel.from_pretrained(model_name)
+except Exception as e:
+    print(f"Error loading model: {e}")
+
+class TextRequest(BaseModel):
+    text: str
+
+# Function to generate embeddings
+def get_embeddings(text: str):
+    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
+    with torch.no_grad():
+        outputs = model(**inputs)
+    embeddings = outputs.last_hidden_state.mean(dim=1)  # Pooling
+    return embeddings.numpy().tolist()  # Convert to list for API response
+
+@app.post("/get-embedding/")
+async def get_embedding(request: TextRequest):
+    text = request.text
+    embeddings = get_embeddings(text)
+    return {"embedding": embeddings}
+
+def gradio_interface(text):
+    return get_embeddings(text)
+
+grn = gr.Interface(fn=gradio_interface, inputs="text", outputs="json", title="Text Embedding Generator")
+
+grn.launch(server_name="0.0.0.0", server_port=7860, share=True)
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=7860)  # Port 7860 for Hugging Face Spaces