Update app.py

app.py

@@ -1,69 +1,70 @@
 import gradio as gr
-from llama_cpp import Llama
+from huggingface_hub import InferenceClient
 from paddleocr import PaddleOCR
 from PIL import Image
 
-# Load GGUF model
-llm = Llama(
-    model_path="./deepseek-v3-0324.Q4_K_M.gguf",  # Make sure this file is in your repo
-    n_ctx=2048,
-    n_threads=8,
-    n_gpu_layers=20  # Set to 0 if you are on CPU-only
-)
-
-# OCR Function
-def ocr_inference(img, lang):
-    ocr = PaddleOCR(use_angle_cls=True, lang=lang, use_gpu=False)
-    result = ocr.ocr(img, cls=True)[0]
-    txts = [line[1][0] for line in result]
-    return " ".join(txts)
+# Use the hosted model
+client = InferenceClient("unsloth/DeepSeek-V3-0324-GGUF")
 
-# Step 1: Convert text to base form words
+# Extract words in base form
 def text_inference(text, language):
     prompt = (
         f"Given the following {language} text, convert each word into its base form. "
         f"Remove all duplicates. Return the base form words as a comma-separated list.\n\n"
         f"Text:\n{text}"
     )
-    response = llm(prompt, max_tokens=256, stop=["</s>"])
-    output_text = response["choices"][0]["text"].strip()
-    words = [w.strip() for w in output_text.split(",") if w.strip()]
+    response = client.text_generation(prompt, max_new_tokens=256, temperature=0.7)
+    words = [w.strip() for w in response.strip().split(",") if w.strip()]
     return words
 
-# Step 2: Generate flashcards for those words
+# Create flashcards
 def make_flashcards(words, language):
     prompt = (
         f"For each {language} word in the list, write a flashcard in this format:\n"
-        f"word - definition - example sentence - translated sentence.\n\n"
+        f"Word: <word>\nDefinition: <definition>\nExample: <sentence>\nTranslation: <translation>\n\n"
         f"Words:\n{', '.join(words)}"
     )
-    response = llm(prompt, max_tokens=512, stop=["</s>"])
-    return response["choices"][0]["text"].strip()
+    response = client.text_generation(prompt, max_new_tokens=512, temperature=0.7)
+    return response.strip()
+
+# OCR from image
+def ocr_inference(img_path, lang_code):
+    ocr = PaddleOCR(use_angle_cls=True, lang=lang_code, use_gpu=False)
+    result = ocr.ocr(img_path, cls=True)[0]
+    return " ".join([line[1][0] for line in result])
 
-# Wrapper logic to handle OCR or text
+# Combined pipeline
 def flashcard_pipeline(text, image, language):
+    lang_code = {
+        "korean": "korean",
+        "japanese": "japan",
+        "chinese": "ch",
+        "english": "en",
+    }.get(language.lower(), "en")
+
     if image:
-        text = ocr_inference(image, language)
+        text = ocr_inference(image, lang_code)
     if not text:
         return "", "Please provide either text or an image."
+
     words = text_inference(text, language)
     flashcards = make_flashcards(words, language)
     return "\n".join(words), flashcards
 
-# Gradio UI
+# Gradio app
 demo = gr.Interface(
     fn=flashcard_pipeline,
     inputs=[
-        gr.Textbox(label="Input Text (leave empty to use image)", lines=4, placeholder="Type or paste sentence here..."),
-        gr.Image(label="Upload Image for OCR (optional)", type="filepath"),
-        gr.Dropdown(choices=["korean", "japan", "french", "ch"], label="Language (for OCR and LLM)")
+        gr.Textbox(label="Input Text (leave blank if using image)", lines=4, placeholder="e.g. 표현이 서툰 것도 잘못인가요..."),
+        gr.Image(type="filepath", label="Upload Image (optional, for OCR)"),
+        gr.Dropdown(["korean", "japanese", "chinese", "english"], label="Language"),
     ],
     outputs=[
         gr.Textbox(label="Base Form Words"),
         gr.Textbox(label="Flashcards"),
     ],
-    title="Language Flashcard Generator (with OCR + DeepSeek GGUF)",
-    description="Either input text or upload an image. The app will extract words, lemmatize them, and generate flashcards."
+    title="📘 Language Flashcard Generator (OCR + LLM)",
+    description="Input text or image. It extracts words, finds base forms, and generates flashcards using DeepSeek-V3-0324.",
 )
 
 if __name__ == "__main__":