update main

main.py

@@ -36,10 +36,10 @@ app.add_middleware(
 
 from transformers import VisionEncoderDecoderModel, ViTImageProcessor, AutoTokenizer
 try:
-    interpreter =1 #pipeline("image-to-text", model="nlpconnect/vit-gpt2-image-captioning")
-    interpreter_model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
-    interpreter_processor = ViTImageProcessor.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
-    interpreter_tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
+    interpreter =pipeline("image-to-text", model="Salesforce/blip-image-captioning-base")
+    #interpreter_model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
+    #interpreter_processor = ViTImageProcessor.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
+    #interpreter_tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
 except Exception as exp:
     print("[ERROR] Can't load nlpconnect/vit-gpt2-image-captioning")
     print(str(exp))
@@ -63,7 +63,7 @@ except Exception as exp:
 
 
 try:
-    generator = pipeline("text-generation", model="deepseek-ai/deepseek-coder-1.3b-instruct", device_map="auto")
+    generator = pipeline("text-generation", model="deepseek-ai/deepseek-coder-1.3b-instruct")
 except Exception as exp:
     print("[ERROR] Can't load deepseek-ai/deepseek-coder-1.3b-instruct ")
     print(str(exp))
@@ -93,17 +93,7 @@ def index(req:Request):
 def index(req:Request):
     return  templates.TemplateResponse('ImageInterpretation.html',{'request':req})
 
-@app.post("/caption2")
-async def generate_caption(file: UploadFile = File(...)):
-    contents = await file.read()
-    image = Image.open(io.BytesIO(contents)).convert("RGB")
 
-    # توليد caption
-    pixel_values = interpreter_processor(images=image, return_tensors="pt").pixel_values
-    output_ids = interpreter_model.generate(pixel_values, max_length=16, num_beams=4)
-    caption = interpreter_tokenizer.decode(output_ids[0], skip_special_tokens=True)
-
-    return {"caption": caption}
 @app.post('/get')
 def g(f:str):
    return generator(f)[0]["generated_text"]