about ready to give the fk up

app.py

@@ -8,8 +8,11 @@ from transformers import AutoTokenizer, AutoModelForCausalLM, AutoProcessor
 import tensorflow as tf
 import tensorflow_hub as hub
 import io
+import os 
+import numpy as np
 from sklearn.metrics.pairwise import cosine_similarity
-import tempfile # Add this import
+import tempfile 
+import shutil
 import logging
 
 # Configure logging
@@ -73,68 +76,39 @@ def save_dataframe_to_csv(df):
     # Return the file path (no need to reopen the file with "rb" mode)
     return temp_file_path
 
-# Main function to perform image captioning and image-text matching
 def process_images_and_statements(image_file):
-    image_file = io.BytesIO(image_file.read())  # Convert temporary file to a byte stream
-    image = Image.open(image_file)
+    with tempfile.NamedTemporaryFile(delete=False) as temp_file:
+        shutil.copyfileobj(image_file, temp_file)
+
+    image = Image.open(temp_file.name)
     image = np.array(image)
     logging.info('Starting process_images_and_statements')
 
-    # Generate image caption for the uploaded image using git-large-r-textcaps
-    caption = generate_caption(git_processor_large_textcaps, git_model_large_textcaps, image)
-
-    # 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 list to store the results
-    results_list = []
-
-    # Loop through each predefined statement
-    for statement in statements:
-        # Compute textual similarity between caption and statement
-        textual_similarity_score = (compute_textual_similarity(caption, statement) * 100) # Multiply by 100
-
-        # Compute ITM score for the image-statement pair
-        itm_score_statement = (compute_itm_score(image, statement) * 100) # Multiply by 100
-
-        # 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 results_list
-        results_list.append({
-            'File Name': file.name, # Include the file name
-            'Statement': statement,
-            'Generated Caption': caption,
-            'Textual Similarity Score': f"{textual_similarity_score:.2f}%",
-            'ITM Score': f"{itm_score_statement:.2f}%",
-            'Final Combined Score': f"{final_score:.2f}%"
-        })
-
-    # Convert the results_list to a DataFrame using pandas.concat
-    results_df = pd.concat([pd.DataFrame([result]) for result in results_list], ignore_index=True)
+    # Generate the image caption
+    generated_caption = caption_image(image)
 
-    logging.info('Finished process_images_and_statements')
+    # Match the statements
+    matched_statements = match_statements(image, statements)
 
-    # Save results_df to a CSV file
-    csv_results = save_dataframe_to_csv(results_df)
+    os.unlink(temp_file.name)  # Remove the temporary file
 
-    # Return both the DataFrame and the CSV data for the Gradio interface
-    return results_df, csv_results
+    return generated_caption, matched_statements
 
-# Gradio interface
-image_input = gr.inputs.File(label="Upload Image")
-output_df = gr.outputs.Dataframe(type="pandas", label="Results")
-output_csv = gr.outputs.File(label="Download CSV")
+# Define Gradio interface
+image_input = gr.inputs.Image(type="file", label="Upload Image")
+text_input = gr.inputs.Textbox(lines=5, label="Enter Statements (one per line)")
+outputs = [
+    gr.outputs.Textbox(label="Generated Caption"),
+    gr.outputs.Textbox(lines=5, label="Matched Statements"),
+]
 
 iface = gr.Interface(
     fn=process_images_and_statements,
-    inputs=image_input,
-    outputs=[output_df, output_csv],
-    title="Image Captioning and Image-Text Matching",
-    theme='sudeepshouche/minimalist',
-    css=".output { flex-direction: column; } .output .outputs { width: 100%; }" # Custom CSS
+    inputs=[image_input, text_input],
+    outputs=outputs,
+    title="Image Captioning and Matching",
+    description="Upload an image and enter statements to generate a caption for the image and match the statements.",
 )
 
+# Launch Gradio app
 iface.launch()