Delete app.py

app.py

@@ -1,61 +0,0 @@
-import os
-import pandas as pd
-import gradio as gr
-from llama_cpp import Llama
-
-# Path to GGUF file
-MODEL_PATH = "./models/llama-3-8b-instruct.Q4_K_M.gguf"
-
-os.makedirs("models", exist_ok=True)
-with open("models/.keep", "w") as f:
-    f.write("placeholder")
-    
-# Load quantized model with llama-cpp
-llm = Llama(
-    model_path=MODEL_PATH,
-    n_ctx=2048,
-    n_threads=8,
-    verbose=False
-)
-
-def build_prompt(source, translation):
-    return (
-        f"<|begin_of_text|><|system|>\n"
-        "You are a helpful assistant that evaluates translation quality. "
-        "Score the quality from 0 (worst) to 1 (best).\n"
-        "<|user|>\n"
-        f"Original: {source}\nTranslation: {translation}\n"
-        "How good is the translation?\n<|assistant|>"
-    )
-
-def estimate_score(source, translation):
-    prompt = build_prompt(source, translation)
-    output = llm(prompt, max_tokens=10, stop=["</s>", "\n"])
-    text = output["choices"][0]["text"].strip()
-
-    try:
-        score = float([s for s in text.split() if s.replace('.', '', 1).isdigit()][-1])
-        score = round(score, 3)
-    except:
-        score = "N/A"
-    return score
-
-def process_file(file):
-    df = pd.read_csv(file.name, sep="\t")
-    scores = []
-    for _, row in df.iterrows():
-        score = estimate_score(row["original"], row["translation"])
-        scores.append(score)
-    df["predicted_score"] = scores
-    return df
-
-demo = gr.Interface(
-    fn=process_file,
-    inputs=gr.File(label="Upload dev.tsv with 'original' and 'translation' columns"),
-    outputs=gr.Dataframe(),
-    title="LLaMA 3 8B (Quantized) MT QE",
-    description="Translation Quality Estimation using LLaMA-3-8B GGUF via llama-cpp-python on CPU"
-)
-
-if __name__ == "__main__":
-    demo.launch()