Update app.py

app.py

@@ -1,139 +1,139 @@
-# app.py
-import os
-import gradio as gr
-import torch
-from PIL import Image
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import timm
-from torchvision import transforms
-from llama_cpp import Llama
-from peft import PeftModel
-
-# 1. Model Definitions (Same as in training script)
-class SigLIPImageEncoder(torch.nn.Module):
-    def __init__(self, model_name='resnet50', embed_dim=512, pretrained_path=None):
-        super().__init__()
-        self.model = timm.create_model(model_name, pretrained=False, num_classes=0, global_pool='avg') # pretrained=False
-        self.embed_dim = embed_dim
-        self.projection = torch.nn.Linear(self.model.num_features, embed_dim)
-
-        if pretrained_path:
-            self.load_state_dict(torch.load(pretrained_path, map_location=torch.device('cpu'))) # Load to CPU first
-            print(f"Loaded SigLIP image encoder from {pretrained_path}")
-        else:
-            print("Initialized SigLIP image encoder without pretrained weights.")
-
-    def forward(self, image):
-        features = self.model(image)
-        embedding = self.projection(features)
-        return embedding
-
-# 2. Load Models and Tokenizer
-phi3_model_path = "QuantFactory/Phi-3-mini-4k-instruct-GGUF"  # Path to your quantized Phi-3 GGUF model
-peft_model_path = "./qlora_phi3_model"
-image_model_name = 'resnet50'
-image_embed_dim = 512
-siglip_pretrained_path = "image_encoder.pth" # Path to your pretrained SigLIP model
-
-device = torch.device("cpu") # Force CPU
-print(f"Using device: {device}")
-
-# Load Tokenizer (using a compatible tokenizer)
-text_tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-4k-instruct", trust_remote_code=True) # Or a compatible tokenizer
-text_tokenizer.pad_token = text_tokenizer.eos_token # Important for training
-
-# Image Transformations
-image_transform = transforms.Compose([
-    transforms.Resize((224, 224)),
-    transforms.ToTensor(),
-    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-])
-
-# Load SigLIP Image Encoder
-image_encoder = SigLIPImageEncoder(model_name=image_model_name, embed_dim=image_embed_dim, pretrained_path=siglip_pretrained_path).to(device)
-image_encoder.eval() # Set to evaluation mode
-
-# Load Phi-3 model using llama.cpp
-#base_model = Llama(
-#    model_path=phi3_model_path,
-#    n_gpu_layers=0,  # Ensure no GPU usage
-#    n_ctx=2048,       # Adjust context length as needed
-#    verbose=True,
-#)
-
-
-#base_model = Llama.from_pretrained(
-#	repo_id="QuantFactory/Phi-3-mini-4k-instruct-GGUF",
-#	filename="Phi-3-mini-4k-instruct.Q2_K.gguf",
-#    n_gpu_layers=0,
-#    n_ctx=2048,
-#    verbose=True 
-#)
-
-base_model_name="microsoft/Phi-3-mini-4k-instruct"
-device = "cpu"
-
-base_model = AutoModelForCausalLM.from_pretrained(base_model_name, torch_dtype=torch.float32, device_map={"": device})
-
-
-# Load and merge
-model = PeftModel.from_pretrained(base_model, peft_model_path, offload_dir='./offload')
-model = model.merge_and_unload()
-print("phi-3 model loaded sucessfully")  
-# 3. Inference Function
-def predict(image, question):
-    """
-    Takes an image and a question as input and returns an answer.
-    """
-    if image is None or question is None or question == "":
-        return "Please provide both an image and a question."
-
-    try:
-        image = Image.fromarray(image).convert("RGB")
-        image = image_transform(image).unsqueeze(0).to(device)
-
-        # Get image embeddings
-        with torch.no_grad():
-            image_embeddings = image_encoder(image)
-            # Flatten the image embeddings for simplicity
-            image_embeddings = image_embeddings.flatten().tolist()
-
-        # Create the prompt with image embeddings
-        prompt = f"Question: {question}\nImage Embeddings: {image_embeddings}\nAnswer:"
-
-        # Generate answer using llama.cpp
-        output = model(
-            prompt,
-            max_tokens=128,
-            stop=["Q:", "\n"],
-            echo=False,
-        )
-
-        answer = output["choices"][0]["text"].strip()
-
-        return answer
-
-    except Exception as e:
-        return f"An error occurred: {str(e)}"
-
-# 4. Gradio Interface
-iface = gr.Interface(
-    fn=predict,
-    inputs=[
-        gr.Image(label="Upload an Image"),
-        gr.Textbox(label="Ask a Question about the Image", placeholder="What is in the image?")
-    ],
-    outputs=gr.Textbox(label="Answer"),
-    title="Image Question Answering with Phi-3 and SigLIP (CPU)",
-    description="Ask questions about an image and get answers powered by Phi-3 (llama.cpp) and SigLIP.",
-    examples=[
-        ["cat_0006.png", "Create a interesting story about this image?"],
-        ["bird_0004.png", "Can you describe this image?"],
-        ["truck_0003.png", "Elaborate the setting of the image"],
-        ["ship_0007.png", "Explain the purpose of image"]
-    ]
-)
-
-# 5. Launch the App
-if __name__ == "__main__":
+# app.py
+import os
+import gradio as gr
+import torch
+from PIL import Image
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import timm
+from torchvision import transforms
+from llama_cpp import Llama
+from peft import PeftModel
+
+# 1. Model Definitions (Same as in training script)
+class SigLIPImageEncoder(torch.nn.Module):
+    def __init__(self, model_name='resnet50', embed_dim=512, pretrained_path=None):
+        super().__init__()
+        self.model = timm.create_model(model_name, pretrained=False, num_classes=0, global_pool='avg') # pretrained=False
+        self.embed_dim = embed_dim
+        self.projection = torch.nn.Linear(self.model.num_features, embed_dim)
+
+        if pretrained_path:
+            self.load_state_dict(torch.load(pretrained_path, map_location=torch.device('cpu'))) # Load to CPU first
+            print(f"Loaded SigLIP image encoder from {pretrained_path}")
+        else:
+            print("Initialized SigLIP image encoder without pretrained weights.")
+
+    def forward(self, image):
+        features = self.model(image)
+        embedding = self.projection(features)
+        return embedding
+
+# 2. Load Models and Tokenizer
+phi3_model_path = "QuantFactory/Phi-3-mini-4k-instruct-GGUF"  # Path to your quantized Phi-3 GGUF model
+peft_model_path = "./qlora_phi3_model"
+image_model_name = 'resnet50'
+image_embed_dim = 512
+siglip_pretrained_path = "image_encoder.pth" # Path to your pretrained SigLIP model
+
+device = torch.device("cpu") # Force CPU
+print(f"Using device: {device}")
+
+# Load Tokenizer (using a compatible tokenizer)
+text_tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-4k-instruct", trust_remote_code=True) # Or a compatible tokenizer
+text_tokenizer.pad_token = text_tokenizer.eos_token # Important for training
+
+# Image Transformations
+image_transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+])
+
+# Load SigLIP Image Encoder
+image_encoder = SigLIPImageEncoder(model_name=image_model_name, embed_dim=image_embed_dim, pretrained_path=siglip_pretrained_path).to(device)
+image_encoder.eval() # Set to evaluation mode
+
+# Load Phi-3 model using llama.cpp
+#base_model = Llama(
+#    model_path=phi3_model_path,
+#    n_gpu_layers=0,  # Ensure no GPU usage
+#    n_ctx=2048,       # Adjust context length as needed
+#    verbose=True,
+#)
+
+
+#base_model = Llama.from_pretrained(
+#	repo_id="QuantFactory/Phi-3-mini-4k-instruct-GGUF",
+#	filename="Phi-3-mini-4k-instruct.Q2_K.gguf",
+#    n_gpu_layers=0,
+#    n_ctx=2048,
+#    verbose=True 
+#)
+
+base_model_name="microsoft/Phi-3-mini-4k-instruct"
+device = "cuda"
+
+base_model = AutoModelForCausalLM.from_pretrained(base_model_name, torch_dtype=torch.float32, device_map={"": device})
+
+
+# Load and merge
+model = PeftModel.from_pretrained(base_model, peft_model_path, offload_dir='./offload')
+model = model.merge_and_unload()
+print("phi-3 model loaded sucessfully")  
+# 3. Inference Function
+def predict(image, question):
+    """
+    Takes an image and a question as input and returns an answer.
+    """
+    if image is None or question is None or question == "":
+        return "Please provide both an image and a question."
+
+    try:
+        image = Image.fromarray(image).convert("RGB")
+        image = image_transform(image).unsqueeze(0).to(device)
+
+        # Get image embeddings
+        with torch.no_grad():
+            image_embeddings = image_encoder(image)
+            # Flatten the image embeddings for simplicity
+            image_embeddings = image_embeddings.flatten().tolist()
+
+        # Create the prompt with image embeddings
+        prompt = f"Question: {question}\nImage Embeddings: {image_embeddings}\nAnswer:"
+
+        # Generate answer using llama.cpp
+        output = model(
+            prompt,
+            max_tokens=128,
+            stop=["Q:", "\n"],
+            echo=False,
+        )
+
+        answer = output["choices"][0]["text"].strip()
+
+        return answer
+
+    except Exception as e:
+        return f"An error occurred: {str(e)}"
+
+# 4. Gradio Interface
+iface = gr.Interface(
+    fn=predict,
+    inputs=[
+        gr.Image(label="Upload an Image"),
+        gr.Textbox(label="Ask a Question about the Image", placeholder="What is in the image?")
+    ],
+    outputs=gr.Textbox(label="Answer"),
+    title="Image Question Answering with Phi-3 and SigLIP (CPU)",
+    description="Ask questions about an image and get answers powered by Phi-3 (llama.cpp) and SigLIP.",
+    examples=[
+        ["cat_0006.png", "Create a interesting story about this image?"],
+        ["bird_0004.png", "Can you describe this image?"],
+        ["truck_0003.png", "Elaborate the setting of the image"],
+        ["ship_0007.png", "Explain the purpose of image"]
+    ]
+)
+
+# 5. Launch the App
+if __name__ == "__main__":
     iface.launch()