Update app.py

app.py

@@ -3,10 +3,31 @@ import requests
 from paddleocr import PaddleOCR, draw_ocr
 from PIL import Image
 import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
 
 img = "input_data/ocr_input/japan1.jpg"
 
-def inference(img, lang):
+model_id = "deepseek-ai/deepseek-llm-7b-chat"
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype=torch.float16, trust_remote_code=True)
+
+def text_inference(text, language):
+    system_prompt = (
+        f"Given the following {language} text, extract all words in their base (dictionary) form, including verbs, adjectives, nouns, and particles. "
+        "Remove all duplicates. Return the base form words as a comma-separated list, and nothing else."
+    )
+    user_prompt = f"{system_prompt}\n\nText:\n{text}"
+
+    input_ids = tokenizer.apply_chat_template([{"role": "user", "content": user_prompt}], return_tensors="pt").to(model.device)
+    output_ids = model.generate(input_ids, max_new_tokens=256)
+    output_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+
+    # Parse comma-separated string into list
+    words = [word.strip() for word in output_text.split(",") if word.strip()]
+    return words
+
+def ocr_inference(img, lang):
 	ocr = PaddleOCR(use_angle_cls=True, lang=lang,use_gpu=False)
 	img_path = img  
 	result = ocr.ocr(img_path, cls=True)[0]
@@ -16,6 +37,5 @@ def inference(img, lang):
 	scores = [line[1][1] for line in result]
 	return txts
 
-
-print(inference(img,"japan"))
-#balls
+def make_flashcards(words):
+    pass;