backup

.gitattributes

@@ -33,3 +33,8 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+examples/frame_000168.jpg filter=lfs diff=lfs merge=lfs -text
+examples/1727808849.jpg filter=lfs diff=lfs merge=lfs -text
+examples/1727809389.jpg filter=lfs diff=lfs merge=lfs -text
+examples/Birch[[:space:]]MWF014-0001.jpg filter=lfs diff=lfs merge=lfs -text
+examples/frame_000036.jpg filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,6 +1,6 @@
 ---
 title: FireNet LLama 3.2
-emoji: 😻
+emoji: 🔥
 colorFrom: gray
 colorTo: red
 sdk: gradio

app.py

@@ -1,14 +1,16 @@
+import base64
+import io
+import json
 import os
+
 import gradio as gr
+import matplotlib.pyplot as plt
+import spaces
 import torch
-from PIL import Image
-from transformers import MllamaForConditionalGeneration, AutoProcessor
-from peft import PeftModel
 from huggingface_hub import login
-import json
-import matplotlib.pyplot as plt
-import io
-import base64
+from peft import PeftModel
+from PIL import Image
+from transformers import AutoProcessor, MllamaForConditionalGeneration
 
 
 def check_environment():
@@ -22,44 +24,19 @@ def check_environment():
         )
 
 
-# # Login to Hugging Face
-# check_environment()
-# login(token=os.environ["HF_TOKEN"], add_to_git_credential=True)
-
-# Load model and processor (do this outside the inference function to avoid reloading)
-# base_model_path = (
-#     "taesiri/BugsBunny-LLama-3.2-11B-Vision-BaseCaptioner-Medium-FullModel"
-# )
+# Login to Hugging Face
+check_environment()
+login(token=os.environ["HF_TOKEN"], add_to_git_credential=True)
 
-# processor = AutoProcessor.from_pretrained(base_model_path)
-# model = MllamaForConditionalGeneration.from_pretrained(
-#     base_model_path,
-#     torch_dtype=torch.bfloat16,
-#     device_map="cuda",
-#     cache_dir="./",
-# )
-# # 
-# odel = PeftModel.from_pretrained(model, lora_weights_path)
 
-
-from transformers import MllamaForConditionalGeneration, AutoProcessor
 import torch
+from transformers import AutoProcessor, MllamaForConditionalGeneration
 
-
-local_model_path = "../merged-llama-3.2-dummy"
-
-# Load model and processor (do this outside the inference function to avoid reloading)
-base_model_path = (
-    local_model_path
-)
-# lora_weights_path = "taesiri/BugsBunny-LLama-3.2-11B-Vision-Base-Medium-LoRA"
+base_model_path = "taesiri/FireNet-LLama-3.2-11B-Vision-Base"
 
 processor = AutoProcessor.from_pretrained(base_model_path)
 model = MllamaForConditionalGeneration.from_pretrained(
-    base_model_path,
-    torch_dtype=torch.bfloat16,
-    device_map="cuda",
-    cache_dir="./"
+    base_model_path, torch_dtype=torch.bfloat16, device_map="cuda"
 )
 
 model.tie_weights()
@@ -94,6 +71,7 @@ def create_color_palette_image(colors):
         return None
 
 
+@spaces.GPU
 def inference(image):
     if image is None:
         return ["Please provide an image"] * 4
@@ -111,7 +89,10 @@ def inference(image):
             "role": "user",
             "content": [
                 {"type": "image"},
-                {"type": "text", "text": "Analyze this image for fire, smoke, haze, or other related conditions."},
+                {
+                    "type": "text",
+                    "text": "Analyze this image for fire, smoke, haze, or other related conditions.",
+                },
             ],
         }
     ]
@@ -139,12 +120,12 @@ def inference(image):
     try:
         json_str = result.strip().split("assistant\n")[1].strip()
         parsed_json = json.loads(json_str)
-        
+
         # Create specific JSON subsets for each section
         fire_analysis = {
             "predictions": parsed_json.get("predictions", "N/A"),
             "description": parsed_json.get("description", "No description available"),
-            "confidence_scores": parsed_json.get("confidence_score", {})
+            "confidence_scores": parsed_json.get("confidence_score", {}),
         }
 
         environment_analysis = {
@@ -153,12 +134,14 @@ def inference(image):
 
         detection_analysis = {
             "detections": parsed_json.get("detections", []),
-            "detection_count": len(parsed_json.get("detections", []))
+            "detection_count": len(parsed_json.get("detections", [])),
         }
 
         report_analysis = {
             "uncertainty_factors": parsed_json.get("uncertainty_factors", []),
-            "false_positive_indicators": parsed_json.get("false_positive_indicators", [])
+            "false_positive_indicators": parsed_json.get(
+                "false_positive_indicators", []
+            ),
         }
 
         return (
@@ -169,7 +152,7 @@ def inference(image):
             json_str,
             "",
             "Analysis complete",
-            parsed_json
+            parsed_json,
         )
     except Exception as e:
         print("DEBUG: Error processing response:", e)
@@ -181,7 +164,7 @@ def inference(image):
             str(result),
             str(e),
             "Error",
-            {}
+            {},
         )
 
 
@@ -197,17 +180,19 @@ with gr.Blocks() as demo:
                 elem_id="large-image",
             )
             submit_btn = gr.Button("Analyze Image", variant="primary")
-            
-            # Add examples here
+
+            # Updated examples
             gr.Examples(
                 examples=[
-                    "examples/Birch MWF014-0001.png",
-                    "examples/Birch MWF014-0006.png",
-                    "examples/Blackstone PB-0010.png",
+                    "examples/1727808849.jpg",
+                    "examples/1727809389.jpg",
+                    "examples/Birch MWF014-0001.jpg",
+                    "examples/frame_000036.jpg",
+                    "examples/frame_000168.jpg",
                 ],
                 inputs=image_input,
                 label="Example Images",
-                examples_per_page=4
+                examples_per_page=5,
             )
 
     with gr.Tabs() as tabs:
@@ -216,30 +201,26 @@ with gr.Blocks() as demo:
                 with gr.Column():
                     fire_output = gr.JSON(
                         label="Fire Details",
-                        lines=4,
                     )
                 with gr.Column():
                     environment_output = gr.JSON(
                         label="Environment Details",
-                        lines=4,
                     )
             with gr.Row():
                 with gr.Column():
                     detection_output = gr.JSON(
                         label="Detection Details",
-                        lines=4,
                     )
                 with gr.Column():
                     report_output = gr.JSON(
                         label="Report Details",
-                        lines=4,
                     )
 
         with gr.Tab("JSON Output", id=0):
             json_output = gr.JSON(
                 label="Detailed JSON Results",
             )
-            
+
         with gr.Tab("Raw Output"):
             raw_output = gr.Textbox(
                 label="Raw JSON Response",
@@ -264,4 +245,4 @@ with gr.Blocks() as demo:
         ],
     )
 
-demo.launch(share=True)
+demo.launch(share=True)

requirements.txt

@@ -0,0 +1,8 @@
+torch
+torchvision
+datasets
+git+https://github.com/huggingface/transformers.git
+accelerate
+pillow
+gradio
+matplotlib