Upload 2 files

app.py

@@ -0,0 +1,63 @@
+import os
+import pandas as pd
+import gradio as gr
+from llama_cpp import Llama
+
+MODEL_PATH = "./models/mistral-7b-instruct-v0.1.Q2_K.gguf"
+
+# Create models folder and placeholder
+os.makedirs("models", exist_ok=True)
+with open("models/.keep", "w") as f:
+    f.write("placeholder")
+
+# Load quantized Mistral model
+llm = Llama(
+    model_path=MODEL_PATH,
+    n_ctx=2048,
+    n_threads=8,
+    verbose=False
+)
+
+def build_prompt(source, translation):
+    return (
+        f"<|system|>
+"
+        "You are a helpful assistant that evaluates translation quality. "
+        "Score the quality from 0 (worst) to 1 (best).
+"
+        "<|user|>
+"
+        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="Mistral 7B Q2_K MT QE",
+    description="Translation Quality Estimation using Mistral-7B-Instruct Q2_K GGUF via llama-cpp-python on CPU"
+)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -0,0 +1,3 @@
+llama-cpp-python==0.2.56
+pandas
+gradio