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change in main
Browse files
main.py
CHANGED
@@ -33,10 +33,10 @@ app.add_middleware(
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allow_headers=["*"],
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)
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try:
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interpreter_model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
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interpreter_processor = ViTImageProcessor.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
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interpreter_tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
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except Exception as exp:
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print("[ERROR] Can't load nlpconnect/vit-gpt2-image-captioning")
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print(str(exp))
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@@ -59,21 +59,21 @@ except Exception as exp:
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#try:
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# generator = pipeline("text-generation", model="deepseek-ai/deepseek-coder-1.3b-instruct", device_map="auto")
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#except Exception as exp:
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# print("[ERROR] Can't load deepseek-ai/deepseek-coder-1.3b-instruct ")
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# print(str(exp))
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try:
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except Exception as exp :
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print("[ERROR] Can't load deepseek-ai/deepseek-coder-1.3b-instruct ")
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print(str(exp))
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app.mount("/static",StaticFiles(directory='static'),'static')
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templates = Jinja2Templates(directory='templates')
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@@ -99,12 +99,12 @@ def caption(file:UploadFile=File(...)):
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if extension not in Supported_extensions:
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return {"error": "Unsupported file type"}
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image = Image.open(file.file)
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pixel_values = interpreter_processor(images=image, return_tensors="pt").pixel_values
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output_ids = interpreter_model.generate(pixel_values, max_length=16, num_beams=4)
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caption = interpreter_tokenizer.decode(output_ids[0], skip_special_tokens=True)
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return {"caption":caption}
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@app.post("/summerize")
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def summerzation(file:UploadFile=File(...)):
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@@ -124,8 +124,8 @@ def summerzation(file:UploadFile=File(...)):
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return {"error": "File is empty"}
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result=""
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return {"summary": result}
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@@ -164,7 +164,7 @@ error.
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##Prompt: {prompt}.
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"""
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output =
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match = re.search(r'```python(.*?)```', output[0]["generated_text"], re.DOTALL)
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code =''
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if not match:
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allow_headers=["*"],
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)
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try:
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interpreter = pipeline("image-to-text", model="nlpconnect/vit-gpt2-image-captioning")
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#interpreter_model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
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#interpreter_processor = ViTImageProcessor.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
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#interpreter_tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
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except Exception as exp:
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print("[ERROR] Can't load nlpconnect/vit-gpt2-image-captioning")
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print(str(exp))
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try:
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generator = pipeline("text-generation", model="deepseek-ai/deepseek-coder-1.3b-instruct", device_map="auto")
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except Exception as exp:
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print("[ERROR] Can't load deepseek-ai/deepseek-coder-1.3b-instruct ")
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print(str(exp))
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#try:
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# generator_model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-1.3b-instruct", trust_remote_code=True)
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# tokengenerator_modelizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-1.3b-instruct", trust_remote_code=True)
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#except Exception as exp :
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# print("[ERROR] Can't load deepseek-ai/deepseek-coder-1.3b-instruct ")
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# print(str(exp))
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app.mount("/static",StaticFiles(directory='static'),'static')
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templates = Jinja2Templates(directory='templates')
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if extension not in Supported_extensions:
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return {"error": "Unsupported file type"}
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image = Image.open(file.file)
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caption = interpreter(image)
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#pixel_values = interpreter_processor(images=image, return_tensors="pt").pixel_values
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#output_ids = interpreter_model.generate(pixel_values, max_length=16, num_beams=4)
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#caption = interpreter_tokenizer.decode(output_ids[0], skip_special_tokens=True)
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#return {"caption":caption}
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return {"caption": caption[0]['generated_text']}
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@app.post("/summerize")
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def summerzation(file:UploadFile=File(...)):
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return {"error": "File is empty"}
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result=""
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for i in range(0,len(text),1024):
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result+=summarizer(text, max_length=150, min_length=30, do_sample=False)[0]['summary_text']
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return {"summary": result}
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##Prompt: {prompt}.
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"""
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output = generator(message, max_length=1000)
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match = re.search(r'```python(.*?)```', output[0]["generated_text"], re.DOTALL)
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code =''
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if not match:
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