Update app.py

app.py

@@ -1,57 +1,57 @@
-from fastapi import FastAPI
-import torch
-from transformers import CLIPProcessor, CLIPModel
-from dotenv import load_dotenv
-import logging
-import os
-
-load_dotenv()
-
-
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-app = FastAPI(title="Text Embedding API",
-              description="Returns CLIP text embeddings via GET")
-
-
-HF_TOKEN = os.getenv('hf_token')
-
-logger.info("Loading CLIP processor and model...")
-try:
-    processor = CLIPProcessor.from_pretrained(
-        "openai/clip-vit-large-patch14", use_auth_token=HF_TOKEN)
-    clip_model = CLIPModel.from_pretrained(
-        "openai/clip-vit-large-patch14", use_auth_token=HF_TOKEN)
-    clip_model.eval()
-    logger.info("CLIP model loaded successfully")
-except Exception as e:
-    logger.error(f"Failed to load CLIP model: {e}")
-    raise
-
-
-def get_text_embedding(text: str):
-    logger.info(f"Processing text: {text}")
-    try:
-        inputs = processor(text=[text], return_tensors="pt",
-                           padding=True, truncation=True)
-        with torch.no_grad():
-            text_embedding = clip_model.get_text_features(**inputs)
-        logger.info("Text embedding generated")
-        return text_embedding.squeeze(0).tolist()
-    except Exception as e:
-        logger.error(f"Error generating embedding: {e}")
-        raise
-
-
-@app.get("/")
-async def root():
-    logger.info("Root endpoint accessed")
-    return {"message": "Welcome to the Text Embedding API. Use GET /embedding?text=your_text to get embeddings."}
-
-
-@app.get("/embedding")
-async def get_embedding(text: str):
-    logger.info(f"Embedding endpoint called with text")
-    embedding = get_text_embedding(text)
-    return {"embedding": embedding, "dimension": len(embedding)}
+from fastapi import FastAPI
+import torch
+from transformers import CLIPProcessor, CLIPModel
+from dotenv import load_dotenv
+import logging
+import os
+
+load_dotenv()
+
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+app = FastAPI(title="Text Embedding API",
+              description="Returns CLIP text embeddings via GET")
+
+
+HF_TOKEN = os.getenv('hf_token')
+
+logger.info("Loading CLIP processor and model...")
+try:
+    processor = CLIPProcessor.from_pretrained(
+        "openai/clip-vit-large-patch14", use_auth_token=HF_TOKEN)
+    clip_model = CLIPModel.from_pretrained(
+        "openai/clip-vit-large-patch14", use_auth_token=HF_TOKEN)
+    clip_model.eval()
+    logger.info("CLIP model loaded successfully")
+except Exception as e:
+    logger.error(f"Failed to load CLIP model: {e}")
+    raise
+
+
+def get_text_embedding(text: str):
+    logger.info(f"Processing text: {text}")
+    try:
+        inputs = processor(text=[text], return_tensors="pt",
+                           padding=True, truncation=True)
+        with torch.no_grad():
+            text_embedding = clip_model.get_text_features(**inputs)
+        logger.info("Text embedding generated")
+        return text_embedding.squeeze(0).tolist()
+    except Exception as e:
+        logger.error(f"Error generating embedding: {e}")
+        raise
+
+
+@app.get("/")
+async def root():
+    logger.info("Root endpoint accessed")
+    return {"message": "Welcome to the Text Embedding API. Use GET https://ashish-001-text-embedding-api.hf.space/embedding?text=your_text to get embeddings."}
+
+
+@app.get("/embedding")
+async def get_embedding(text: str):
+    logger.info(f"Embedding endpoint called with text")
+    embedding = get_text_embedding(text)
+    return {"embedding": embedding, "dimension": len(embedding)}