get rid of dataframe as it was hugely broken

app.py

@@ -9,7 +9,6 @@ import tensorflow as tf
 import tensorflow_hub as hub
 from sklearn.metrics.pairwise import cosine_similarity
 
-
 # Import logging module
 import logging
 
@@ -88,13 +87,13 @@ def process_images_and_statements(image):
     # Generate image caption for the uploaded image using git-large-r-textcaps
     caption = generate_caption(git_processor_large_textcaps, git_model_large_textcaps, image)
 
+    # Initialize an empty list to store the results
+    results = []
+
     # Define weights for combining textual similarity score and image-statement ITM score (adjust as needed)
     weight_textual_similarity = 0.5
     weight_statement = 0.5
 
-    # Initialize an empty DataFrame with column names
-    results_df = pd.DataFrame(columns=['Statement', 'Textual Similarity Score', 'ITM Score', 'Final Combined Score'])
-
     # Loop through each predefined statement
     for statement in statements:
         # Compute textual similarity between caption and statement
@@ -106,18 +105,16 @@ def process_images_and_statements(image):
         # Combine the two scores using a weighted average
         final_score = (weight_textual_similarity * textual_similarity_score) + (weight_statement * itm_score_statement)
 
-        # Append the result to the DataFrame
-        results_df = results_df.append({
-            'Statement': statement,
-            'Textual Similarity Score': textual_similarity_score,
-            'ITM Score': itm_score_statement,
-            'Final Combined Score': final_score
-        }, ignore_index=True)
+        # Store the result
+        result_text = (f'Textual similarity between caption ("{caption}") and statement ("{statement}") is {textual_similarity_score:.3f}\n'
+                       f'The image-statement pair ("{statement}") is matched with a probability of {itm_score_statement:.3%}\n'
+                       f'The final combined score is {final_score:.3%}')
+        results.append(result_text)
 
     logging.info('Finished process_images_and_statements')
 
-    # Convert DataFrame to a formatted string and return it
-    output = results_df.to_string(index=False)
+        # Combine the results and return them
+    output = "\n\n".join(results)
     return output
 
 # Gradio interface
@@ -125,4 +122,4 @@ image_input = gr.inputs.Image()
 output = gr.outputs.Textbox(label="Results")
 
 iface = gr.Interface(fn=process_images_and_statements, inputs=image_input, outputs=output, title="Image Captioning and Image-Text Matching")
-iface.launch()
+iface.launch()