Update app.py

app.py

@@ -599,76 +599,76 @@ def load_and_preprocess_image(img):
     return np.expand_dims(img, axis=0)
 
 
-def generate_caption_coca(image):
-    img_processed = load_and_preprocess_image(image)
+# def generate_caption_coca(image):
+#     img_processed = load_and_preprocess_image(image)
 
-    _, cap_features = coca_model.encoder.predict(img_processed, verbose=0)
-    cap_features = cap_features.astype(np.float32) 
+#     _, cap_features = coca_model.encoder.predict(img_processed, verbose=0)
+#     cap_features = cap_features.astype(np.float32) 
 
-    start_token_id = word_index[start_token]
-    end_token_id = word_index[end_token]
-    sequence = [start_token_id]
-    text_input = np.zeros((1, sentence_length - 1), dtype=np.float32)
+#     start_token_id = word_index[start_token]
+#     end_token_id = word_index[end_token]
+#     sequence = [start_token_id]
+#     text_input = np.zeros((1, sentence_length - 1), dtype=np.float32)
 
-    for t in range(sentence_length - 1):
-        text_input[0, :len(sequence)] = sequence
+#     for t in range(sentence_length - 1):
+#         text_input[0, :len(sequence)] = sequence
 
-        _, logits = coca_model.decoder.predict(
-            [text_input, cap_features],
-            verbose=0
-        )
-        next_token = np.argmax(logits[0, t, :])
+#         _, logits = coca_model.decoder.predict(
+#             [text_input, cap_features],
+#             verbose=0
+#         )
+#         next_token = np.argmax(logits[0, t, :])
 
-        sequence.append(next_token)
-        if next_token == end_token_id or len(sequence) >= (sentence_length - 1):
-            break
+#         sequence.append(next_token)
+#         if next_token == end_token_id or len(sequence) >= (sentence_length - 1):
+#             break
 
-    caption = " ".join(
-        [index_word[token] for token in sequence
-         if token not in {word_index[start_token], word_index[end_token]}]
-    )
+#     caption = " ".join(
+#         [index_word[token] for token in sequence
+#          if token not in {word_index[start_token], word_index[end_token]}]
+#     )
 
-    return caption
+#     return caption
     
     
-# def generate_caption_coca(image):
-#     img_processed = load_and_preprocess_image(image)
-#     _, cap_features = coca_model.encoder.predict(img_processed, verbose=0)
+def generate_caption_coca(image):
+    img_processed = load_and_preprocess_image(image)
+    _, cap_features = coca_model.encoder.predict(img_processed, verbose=0)
 
-#     beams = [([word_index[start_token]], 0.0)]
+    beams = [([word_index[start_token]], 0.0)]
 
-#     for _ in range(max_length):
-#         new_beams = []
-#         for seq, log_prob in beams:
-#             if seq[-1] == word_index[end_token]:
-#                 new_beams.append((seq, log_prob))
-#                 continue
+    for _ in range(max_length):
+        new_beams = []
+        for seq, log_prob in beams:
+            if seq[-1] == word_index[end_token]:
+                new_beams.append((seq, log_prob))
+                continue
 
-#             text_input = np.zeros((1, max_length), dtype=np.int32)
-#             text_input[0, :len(seq)] = seq
+            text_input = np.zeros((1, max_length), dtype=np.int32)
+            text_input[0, :len(seq)] = seq
 
-#             predictions = coca_model.decoder.predict([text_input, cap_features], verbose=0)
-#             _, logits = predictions  
-#             logits = logits[0, len(seq)-1, :] 
-#             probs = np.exp(logits - np.max(logits))
-#             probs /= probs.sum()
+            predictions = coca_model.decoder.predict([text_input, cap_features], verbose=0)
+            _, logits = predictions  
+            logits = logits[0, len(seq)-1, :] 
+            probs = np.exp(logits - np.max(logits))
+            probs /= probs.sum()
 
-#             top_k = np.argsort(-probs)[:beam_width]
-#             for token in top_k:
-#                 new_seq = seq + [token]
-#                 new_log_prob = (log_prob * len(seq) + np.log(probs[token])) / (len(seq) + 1)
+            top_k = np.argsort(-probs)[:beam_width]
+            for token in top_k:
+                new_seq = seq + [token]
+                new_log_prob = (log_prob * len(seq) + np.log(probs[token])) / (len(seq) + 1)
                 
-#                 if has_repeated_ngrams(new_seq, n=2):
-#                     new_log_prob -= 0.5
+                if has_repeated_ngrams(new_seq, n=2):
+                    new_log_prob -= 0.5
 
-#                 new_beams.append((new_seq, new_log_prob))
+                new_beams.append((new_seq, new_log_prob))
 
-#         beams = sorted(new_beams, key=lambda x: x[1], reverse=True)[:beam_width]
-#         if all(beam[0][-1] == word_index[end_token] for beam in beams):
-#             break
+        beams = sorted(new_beams, key=lambda x: x[1], reverse=True)[:beam_width]
+        if all(beam[0][-1] == word_index[end_token] for beam in beams):
+            break
 
-#     best_seq = max(beams, key=lambda x: x[1])[0]
-#     return " ".join(index_word[i] for i in best_seq if i not in {word_index[start_token], word_index[end_token]})
+    best_seq = max(beams, key=lambda x: x[1])[0]
+    return " ".join(index_word[i] for i in best_seq if i not in {word_index[start_token], word_index[end_token]})
 
 
 def generate_caption_rnn(image):