Update app.py

app.py

@@ -1,98 +1,98 @@
-# app.py (Content of this file should be your 'gradio_code_debugged_v2' from previous steps)
-import gradio as gr
-import torch
-import torch.nn as nn
-from transformers import ViTModel, GPT2LMHeadModel, GPT2TokenizerFast, ViTFeatureExtractor, GPT2Config
-from huggingface_hub import hf_hub_download
-from PIL import Image
-import asyncio
-import concurrent.futures
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-# Load Model & Tokenizer
-class ViT_GPT2_Captioner(nn.Module):
-    def __init__(self):
-        super(ViT_GPT2_Captioner, self).__init__()
-        self.vit = ViTModel.from_pretrained('google/vit-base-patch16-224-in21k')
-        gpt2_config = GPT2Config.from_pretrained('gpt2')
-        gpt2_config.add_cross_attention = True
-        self.gpt2 = GPT2LMHeadModel.from_pretrained('gpt2', config=gpt2_config)
-        self.bridge = nn.Linear(self.vit.config.hidden_size, self.gpt2.config.n_embd)
-        for param in self.vit.parameters():
-            param.requires_grad = False
-
-    def forward(self, pixel_values, captions, attention_mask=None):
-        visual_features = self.vit(pixel_values=pixel_values).last_hidden_state
-        projected_features = self.bridge(visual_features[:, 0, :])
-        outputs = self.gpt2(input_ids=captions, attention_mask=attention_mask,
-                                    encoder_hidden_states=projected_features.unsqueeze(1),
-                                    encoder_attention_mask=torch.ones(projected_features.size(0), 1).to(projected_features.device))
-        return outputs.logits
-
-model_path = hf_hub_download(repo_id="ayushrupapara/vit-gpt2-flickr8k-image-captioner", filename="model.pth") # Correct repo_id
-model = ViT_GPT2_Captioner().to(device)
-model.load_state_dict(torch.load(model_path, map_location=device))
-model.eval()
-
-tokenizer = GPT2TokenizerFast.from_pretrained("ayushrupapara/vit-gpt2-flickr8k-image-captioner", force_download=True) # Correct repo_id
-tokenizer.pad_token = tokenizer.eos_token
-feature_extractor = ViTFeatureExtractor.from_pretrained("google/vit-base-patch16-224-in21k")
-
-import asyncio
-import concurrent.futures
-
-executor = concurrent.futures.ThreadPoolExecutor()
-
-# beam search with tunning
-async def generate_caption_async(image, num_beams, temperature): 
-    loop = asyncio.get_event_loop()
-    return await loop.run_in_executor(executor, generate_caption_sync, image, num_beams, temperature) 
-
-def generate_caption_sync(image, num_beams=5, temperature=0.5, max_length=20):
-    #print(f"Received max_length: {max_length}, Type: {type(max_length)}") 
-    max_length = int(max_length) 
-    #print(f"Max_length after int conversion: {max_length}, Type: {type(max_length)}") 
-
-
-    if image is None:
-        return "No image uploaded"
-    if isinstance(image, Image.Image):
-        image = image.convert("RGB")
-    else:
-        raise TypeError("Invalid image format. Expected a PIL Image.")
-
-    pixel_values = feature_extractor(images=image, return_tensors="pt").pixel_values.to(device)
-
-    with torch.no_grad():
-        input_ids = torch.tensor([[tokenizer.eos_token_id]], device=device)
-        output_ids = model.gpt2.generate( # Using model.gpt2.generate for beam search
-            inputs=input_ids,
-            encoder_hidden_states=model.bridge(model.vit(pixel_values=pixel_values).last_hidden_state[:, 0, :]).unsqueeze(1),
-            max_length=max_length, 
-            num_beams=num_beams,     
-            temperature=temperature,   
-            length_penalty=0.9, 
-            no_repeat_ngram_size=2, 
-            early_stopping=True,
-            pad_token_id=tokenizer.eos_token_id,
-            eos_token_id=tokenizer.eos_token_id,
-        )
-
-    caption = tokenizer.decode(output_ids.squeeze(), skip_special_tokens=True)
-    return caption
-
-
-iface = gr.Interface(fn=generate_caption_async, 
-                     inputs=[
-                         gr.Image(type="pil"), 
-                         gr.Slider(minimum=1, maximum=10, step=1, value=5, label="Number of Beams (num_beams)"), 
-                         gr.Slider(minimum=0.1, maximum=2.0, step=0.1, value=0.7, label="Temperature")     
-                     ],
-                     outputs="text",
-                     title="ViT-GPT2 Image Captioning", 
-                     description="Upload an image to get a caption.")
-
-
-
+# app.py (Content of this file should be your 'gradio_code_debugged_v2' from previous steps)
+import gradio as gr
+import torch
+import torch.nn as nn
+from transformers import ViTModel, GPT2LMHeadModel, GPT2TokenizerFast, ViTFeatureExtractor, GPT2Config
+from huggingface_hub import hf_hub_download
+from PIL import Image
+import asyncio
+import concurrent.futures
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Load Model & Tokenizer
+class ViT_GPT2_Captioner(nn.Module):
+    def __init__(self):
+        super(ViT_GPT2_Captioner, self).__init__()
+        self.vit = ViTModel.from_pretrained('google/vit-base-patch16-224-in21k')
+        gpt2_config = GPT2Config.from_pretrained('gpt2')
+        gpt2_config.add_cross_attention = True
+        self.gpt2 = GPT2LMHeadModel.from_pretrained('gpt2', config=gpt2_config)
+        self.bridge = nn.Linear(self.vit.config.hidden_size, self.gpt2.config.n_embd)
+        for param in self.vit.parameters():
+            param.requires_grad = False
+
+    def forward(self, pixel_values, captions, attention_mask=None):
+        visual_features = self.vit(pixel_values=pixel_values).last_hidden_state
+        projected_features = self.bridge(visual_features[:, 0, :])
+        outputs = self.gpt2(input_ids=captions, attention_mask=attention_mask,
+                                    encoder_hidden_states=projected_features.unsqueeze(1),
+                                    encoder_attention_mask=torch.ones(projected_features.size(0), 1).to(projected_features.device))
+        return outputs.logits
+
+model_path = hf_hub_download(repo_id="ayushrupapara/vit-gpt2-flickr8k-image-captioner", filename="model.pth") # Correct repo_id
+model = ViT_GPT2_Captioner().to(device)
+model.load_state_dict(torch.load(model_path, map_location=device))
+model.eval()
+
+tokenizer = GPT2TokenizerFast.from_pretrained("ayushrupapara/vit-gpt2-flickr8k-image-captioner", force_download=True) # Correct repo_id
+tokenizer.pad_token = tokenizer.eos_token
+feature_extractor = ViTFeatureExtractor.from_pretrained("google/vit-base-patch16-224-in21k")
+
+import asyncio
+import concurrent.futures
+
+executor = concurrent.futures.ThreadPoolExecutor()
+
+# beam search with tunning
+async def generate_caption_async(image, num_beams, temperature): 
+    loop = asyncio.get_event_loop()
+    return await loop.run_in_executor(executor, generate_caption_sync, image, num_beams, temperature) 
+
+def generate_caption_sync(image, num_beams=5, temperature=0.5, max_length=20):
+    #print(f"Received max_length: {max_length}, Type: {type(max_length)}") 
+    max_length = int(max_length) 
+    #print(f"Max_length after int conversion: {max_length}, Type: {type(max_length)}") 
+
+
+    if image is None:
+        return "No image uploaded"
+    if isinstance(image, Image.Image):
+        image = image.convert("RGB")
+    else:
+        raise TypeError("Invalid image format. Expected a PIL Image.")
+
+    pixel_values = feature_extractor(images=image, return_tensors="pt").pixel_values.to(device)
+
+    with torch.no_grad():
+        input_ids = torch.tensor([[tokenizer.eos_token_id]], device=device)
+        output_ids = model.gpt2.generate( # Using model.gpt2.generate for beam search
+            inputs=input_ids,
+            encoder_hidden_states=model.bridge(model.vit(pixel_values=pixel_values).last_hidden_state[:, 0, :]).unsqueeze(1),
+            max_length=max_length, 
+            num_beams=num_beams,     
+            temperature=temperature,   
+            length_penalty=0.9, 
+            no_repeat_ngram_size=2, 
+            early_stopping=True,
+            pad_token_id=tokenizer.eos_token_id,
+            eos_token_id=tokenizer.eos_token_id,
+        )
+
+    caption = tokenizer.decode(output_ids.squeeze(), skip_special_tokens=True)
+    return caption
+
+
+iface = gr.Interface(fn=generate_caption_async, 
+                     inputs=[
+                         gr.Image(type="pil"), 
+                         gr.Slider(minimum=1, maximum=10, step=1, value=5, label="Number of Beams"), 
+                         gr.Slider(minimum=0.1, maximum=2.0, step=0.1, value=0.7, label="Temperature")     
+                     ],
+                     outputs="text",
+                     title="ViT-GPT2 Image Captioning", 
+                     description="Upload an image to get a caption.")
+
+
+
 iface.launch() # Removed debug=True for deployment