Create eval.py

eval.py

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+import gradio as gr
+import pandas as pd
+
+# Read source sentences and translations from an Excel file
+excel_file = "demo.xlsx"  # Path to your Excel file
+df = pd.read_excel(excel_file)
+
+# Assuming the Excel file has columns: "Source", "Translation_1", "Translation_2", ..., "Translation_5"
+source_col = "Source Sentence"
+translation_cols = [f"Translation_{i}" for i in range(1, 6)]
+
+# Store the result scores after evaluation
+#result_columns = ['Source', 'Translation', 'Accuracy', 'Fluency', 'Overall']
+result_columns = ['Source', 'Translation', 'Rating']
+
+# Function to simulate the rating collection and return scores
+def evaluate_translations(*ratings):
+    # Initialize lists to store results for all translations
+    sources = []
+    translations = []
+    #accuracy_ratings = []
+    #fluency_ratings = []
+    overall_ratings = []
+    name = ratings[0]
+    for item in ratings:
+        if type(item)==int:
+            overall_ratings.append(item)
+    
+    for idx, row in df.iterrows():
+        source = row[source_col]
+        translations_list = [row[translation_col] for translation_col in translation_cols]
+        
+        # Collect ratings for each translation
+        for i, translation in enumerate(translations_list):
+           
+            sources.append(source)
+            translations.append(translation)
+       
+    # Store the results in a DataFrame
+    results_df = pd.DataFrame({
+        'Source': sources,
+        'Translation': translations,
+        #'Accuracy': accuracy_ratings,
+        #'Fluency': fluency_ratings,
+        'Overall': overall_ratings
+    })
+    
+    # Save the result to an Excel file
+    filename = name+"_evaluation_results.xlsx"
+    results_df.to_excel(filename, index=False)
+    return "Evaluation complete. Results have been saved to" + filename + "."
+
+# Function to create the input sliders dynamically for each translation
+def get_inputs():
+    inputs = []
+    inputs.append(gr.Textbox(label="User Name :", interactive=True))
+    for idx, row in df.iterrows():
+        source = row[source_col]
+        translations_list = [row[translation_col] for translation_col in translation_cols]
+        
+        # Source sentence display (non-editable)
+        
+        inputs.append(gr.Textbox(value=source, label=f"Source Sentence {idx+1}", interactive=False))
+        
+        # Add 3 sliders for each translation (accuracy, fluency, overall)
+        for i, translation in enumerate(translations_list):
+            with gr.Row():
+                inputs.append(gr.Textbox(value=translation, label=f"Translation {i+1}"))
+                #inputs.append(gr.Slider(minimum=1, maximum=5, step=1, label=f"Accuracy (Translation {i+1})"))
+                #inputs.append(gr.Slider(minimum=1, maximum=5, step=1, label=f"Fluency (Translation {i+1})"))
+                inputs.append(gr.Slider(minimum=1, maximum=5, step=1, label=f"Rating (Translation {i+1})"))
+    
+    return inputs
+
+# Define the Gradio interface outputs
+def get_outputs():
+    return gr.Textbox(label="Evaluation Status")
+
+# Create the Gradio interface
+with gr.Blocks() as demo:
+    # Inputs
+    inputs = get_inputs()
+    
+    # Outputs
+    output = get_outputs()
+
+    # Add a Submit Button
+    submit_button = gr.Button("Submit Evaluation")
+    
+    # Bind the button to the evaluation function
+    submit_button.click(evaluate_translations, inputs=inputs, outputs=output)
+
+# Launch the interface
+demo.launch()