Create app.py

app.py

@@ -0,0 +1,55 @@
+import gradio as gr
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+
+HUGGING_FACE_USER_NAME = "elalimy"
+model_name = "my_awesome_peft_finetuned_helsinki_model"
+peft_model_id = f"{HUGGING_FACE_USER_NAME}/{model_name}"
+
+# Load model configuration (assuming it's saved locally)
+config = PeftConfig.from_pretrained(peft_model_id)
+# Load the base model from its local directory (replace with actual model type)
+base_model = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_8bit=False)
+
+# Load the tokenizer from its local directory (replace with actual tokenizer type)
+tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
+
+# Load the Peft model (assuming it's a custom class or adaptation)
+AI_model = PeftModel.from_pretrained(base_model, peft_model_id)
+
+def generate_translation(source_text, device="cpu"):
+    # Encode the source text
+    input_ids = tokenizer.encode(source_text, return_tensors='pt').to(device)
+
+    # Move the model to the same device as input_ids
+    model = base_model.to(device)
+
+    # Generate the translation with adjusted decoding parameters
+    generated_ids = model.generate(
+        input_ids=input_ids,
+        max_length=512,  # Adjust max_length if needed
+        num_beams=4,
+        length_penalty=5,  # Adjust length_penalty if needed
+        no_repeat_ngram_size=4,
+        early_stopping=True
+    )
+
+    # Decode the generated translation excluding special tokens
+    generated_text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
+
+    return generated_text
+
+def translate(text):
+    return generate_translation(text)
+
+# Define the Gradio interface
+iface = gr.Interface(
+    fn=translate,
+    inputs="text",
+    outputs="text",
+    title="Translation App",
+    description="Translate text using a fine-tuned Helsinki model."
+)
+
+# Launch the Gradio app
+iface.launch()