Update app.py

app.py

@@ -1,65 +1,24 @@
 import gradio as gr
-import sys
-import subprocess
-import pkg_resources
-
-def install_package(package):
-    try:
-        subprocess.check_call([sys.executable, "-m", "pip", "install", package])
-        return True
-    except:
-        return False
-
-# Check and install missing dependencies
-required_packages = ["torch", "unbabel-comet"]
-missing_packages = []
-
-for package in required_packages:
-    try:
-        pkg_resources.get_distribution(package)
-    except pkg_resources.DistributionNotFound:
-        missing_packages.append(package)
-
-if missing_packages:
-    print(f"Missing packages: {', '.join(missing_packages)}")
-    for package in missing_packages:
-        if install_package(package):
-            print(f"Successfully installed {package}")
-        else:
-            print(f"Failed to install {package}")
-            print(f"Please install manually: pip install {' '.join(required_packages)}")
-            sys.exit(1)
-
-# Now import torch and comet after ensuring they're installed
-import torch
 from comet import download_model, load_from_checkpoint
 
-def evaluate_translation(src_text, mt_text):
-    if not hasattr(evaluate_translation, "model"):
-        model_path = download_model("wasanx/ComeTH")
-        evaluate_translation.model = load_from_checkpoint(model_path)
-    
-    translations = [{"src": src_text, "mt": mt_text}]
-    results = evaluate_translation.model.predict(
-        translations, 
-        batch_size=1, 
-        gpus=0
-    )
-    return float(results['scores'][0])
-
-demo = gr.Interface(
-    fn=evaluate_translation,
-    inputs=[
-        gr.Textbox(label="English Source Text"),
-        gr.Textbox(label="Thai Translation")
-    ],
-    outputs=gr.Number(label="Quality Score"),
-    examples=[
-        ["This is a test sentence.", "นี่คือประโยคทดสอบ"],
-        ["The weather is nice today.", "อากาศดีมากวันนี้"]
-    ],
-    title="ComeTH Translator Evaluator"
-)
+model_path = download_model("wasanx/ComeTH")
+model = load_from_checkpoint(model_path)
 
-if __name__ == "__main__":
-    demo.launch()
+def score_translation(src_text, mt_text):
+    translations = [{"src": src_text, "mt": mt_text}]
+    results = model.predict(translations, batch_size=1, gpus=1)
+    scores = results.get('scores', [[]])[0]
+    return {f"Score {i+1}": score for i, score in enumerate(scores)}
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Translation Quality Scoring with ComeTH Model")
+    with gr.Row():
+        with gr.Column():
+            src_input = gr.Textbox(label="Source Text (English)")
+            mt_input = gr.Textbox(label="Machine Translation Text (Thai)")
+        with gr.Column():
+            score_output = gr.Label(num_top_classes=5, label="Quality Scores")
+    score_button = gr.Button("Score Translation")
+    score_button.click(fn=score_translation, inputs=[src_input, mt_input], outputs=[score_output])
+
+demo.launch(server_name="0.0.0.0", server_port=7860)