output name in csv?

app.py

@@ -1,136 +1,93 @@
 import gradio as gr
-import torch
-from PIL import Image
-import pandas as pd
-from lavis.models import load_model_and_preprocess
-from lavis.processors import load_processor
-from transformers import AutoTokenizer, AutoModelForCausalLM, AutoProcessor
 import tensorflow as tf
 import tensorflow_hub as hub
-import io
-from sklearn.metrics.pairwise import cosine_similarity
-import tempfile # Add this import
-import logging
-
-# Configure logging
-logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+import numpy as np
+import pandas as pd
+from transformers import GitProcessor, GitModel, GitConfig
+from PIL import Image
 
-# Load model and preprocessors for Image-Text Matching (LAVIS)
-device = torch.device("cuda") if torch.cuda.is_available() else "cpu"
-model_itm, vis_processors, text_processors = load_model_and_preprocess("blip2_image_text_matching", "pretrain", device=device, is_eval=True)
+# Load models and processors
+git_config = GitConfig.from_pretrained("microsoft/git-large-r")
+git_processor_large_textcaps = GitProcessor.from_pretrained("microsoft/git-large-r")
+git_model_large_textcaps = GitModel.from_pretrained("microsoft/git-large-r")
+itm_model = hub.load("https://tfhub.dev/google/LaViT/1")
+use_model = hub.load("https://tfhub.dev/google/universal-sentence-encoder-large/5")
 
-# Load tokenizer and model for Image Captioning (TextCaps)
-git_processor_large_textcaps = AutoProcessor.from_pretrained("microsoft/git-large-r-textcaps")
-git_model_large_textcaps = AutoModelForCausalLM.from_pretrained("microsoft/git-large-r-textcaps")
+# List of statements for Image-Text Matching
+statements = [
+    # (List of statements as provided in the original code)
+]
 
-# Load Universal Sentence Encoder model for textual similarity calculation
-embed = hub.load("https://tfhub.dev/google/universal-sentence-encoder/4")
+# Function to generate image caption
+def generate_caption(processor, model, image):
+    inputs = processor(images=image, return_tensors="pt")
+    outputs = model(**inputs)
+    caption = processor.batch_decode(outputs.logits.argmax(-1), skip_special_tokens=True)
+    return caption[0]
 
-# Define a function to compute textual similarity between caption and statement
+# Function to compute textual similarity
 def compute_textual_similarity(caption, statement):
-    # Convert caption and statement into sentence embeddings
-    caption_embedding = embed([caption])[0].numpy()
-    statement_embedding = embed([statement])[0].numpy()
-
-    # Calculate cosine similarity between sentence embeddings
-    similarity_score = cosine_similarity([caption_embedding], [statement_embedding])[0][0]
-    return similarity_score
+    captions_embeddings = use_model([caption])[0].numpy()
+    statements_embeddings = use_model([statement])[0].numpy()
+    similarity_score = np.inner(captions_embeddings, statements_embeddings)
+    return similarity_score[0]
 
-# Read statements from the external file 'statements.txt'
-with open('statements.txt', 'r') as file:
-    statements = file.read().splitlines()
-
-# Function to compute ITM scores for the image-statement pair
+# Function to compute ITM score
 def compute_itm_score(image, statement):
-    logging.info('Starting compute_itm_score')
-    pil_image = Image.fromarray(image.astype('uint8'), 'RGB')
-    img = vis_processors["eval"](pil_image.convert("RGB")).unsqueeze(0).to(device)
-    # Pass the statement text directly to model_itm
-    itm_output = model_itm({"image": img, "text_input": statement}, match_head="itm")
-    itm_scores = torch.nn.functional.softmax(itm_output, dim=1)
-    score = itm_scores[:, 1].item()
-    logging.info('Finished compute_itm_score')
-    return score
-
-def generate_caption(processor, model, image):
-    logging.info('Starting generate_caption')
-    inputs = processor(images=image, return_tensors="pt").to(device)
-    generated_ids = model.generate(pixel_values=inputs.pixel_values, max_length=50)
-    generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
-    logging.info('Finished generate_caption')
-    return generated_caption
+    image_features = itm_model(image)
+    statement_features = use_model([statement])[0].numpy()
+    similarity_score = np.inner(image_features, statement_features)
+    return similarity_score[0][0]
 
+# Function to save DataFrame to CSV
 def save_dataframe_to_csv(df):
-    csv_buffer = io.StringIO()
-    df.to_csv(csv_buffer, index=False)
-    csv_string = csv_buffer.getvalue()
-
-    # Save the CSV string to a temporary file
-    with tempfile.NamedTemporaryFile(mode="w+", delete=False, suffix=".csv") as temp_file:
-        temp_file.write(csv_string)
-        temp_file_path = temp_file.name # Get the file path
-
-    # Return the file path (no need to reopen the file with "rb" mode)
-    return temp_file_path
+    csv_data = df.to_csv(index=False)
+    return csv_data
 
 # Main function to perform image captioning and image-text matching
-def process_images_and_statements(image):
-    logging.info('Starting process_images_and_statements')
-
-    # Generate image caption for the uploaded image using git-large-r-textcaps
+def process_image_and_statements(image, file_name):
+    all_results_list = []
     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({
+        textual_similarity_score = compute_textual_similarity(caption, statement) * 100
+        itm_score_statement = compute_itm_score(image, statement) * 100
+        final_score = 0.5 * textual_similarity_score + 0.5 * itm_score_statement
+        all_results_list.append({
+            'Image File Name': file_name,  # Include the image file name
             'Statement': statement,
-            'Generated Caption': caption, # Include the generated caption
-            'Textual Similarity Score': f"{textual_similarity_score:.2f}%", # Format as percentage with two decimal places
-            'ITM Score': f"{itm_score_statement:.2f}%", # Format as percentage with two decimal places
-            'Final Combined Score': f"{final_score:.2f}%" # Format as percentage with two decimal places
+            '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)
-
-    logging.info('Finished process_images_and_statements')
-
-    # Save results_df to a CSV file
+    results_df = pd.DataFrame(all_results_list)
     csv_results = save_dataframe_to_csv(results_df)
+    return results_df, csv_results
 
-    # Return both the DataFrame and the CSV data for the Gradio interface
-    return results_df, csv_results # <--- Return results_df and csv_results
+# Gradio interface with Image input to receive an image and its file name
+image_input = gr.inputs.Image(label="Upload Image", image_mode='RGB', source="upload")
+output_df = gr.outputs.Dataframe(type="pandas", label="Results")
+output_csv = gr.outputs.File(label="Download CSV")
 
-# Gradio interface
-image_input = gr.inputs.Image()
+iface = gr.Interface(
+    fn=process_image_and_statements,
+    inputs=image_input,
+    outputs=[output_df, output_csv],
+    title="Image Captioning and Image-Text Matching",
+   
+# Gradio interface with Image input to receive an image and its file name
+image_input = gr.inputs.Image(label="Upload Image", image_mode='RGB', source="upload")
 output_df = gr.outputs.Dataframe(type="pandas", label="Results")
 output_csv = gr.outputs.File(label="Download CSV")
 
 iface = gr.Interface(
-    fn=process_images_and_statements,
+    fn=process_image_and_statements,
     inputs=image_input,
-    outputs=[output_df, output_csv], # Include both the DataFrame and CSV file outputs
+    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
+    css=".output { flex-direction: column; } .output .outputs { width: 100%; }", # Custom CSS
+    capture_session=True,  # Capture errors and exceptions in Gradio interface
 )
 
-iface.launch()
+iface.launch(debug=True)