Upload folder using huggingface_hub

.github/workflows/update_space.yml

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+name: Run Python script
+
+on:
+  push:
+    branches:
+      - main
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+
+    steps:
+    - name: Checkout
+      uses: actions/checkout@v2
+
+    - name: Set up Python
+      uses: actions/setup-python@v2
+      with:
+        python-version: '3.9'
+
+    - name: Install Gradio
+      run: python -m pip install -r requirements.txt
+
+    - name: Log in to Hugging Face
+      run: python -c 'import huggingface_hub; huggingface_hub.login(token="${{ secrets.hf_token }}")'
+
+    - name: Deploy to Spaces
+      run: cd gradio/ && gradio deploy

README.md

@@ -1,12 +1,6 @@
 ---
-title: Echo Chatbot
-emoji: 💻
-colorFrom: gray
-colorTo: blue
-sdk: gradio
-sdk_version: 5.6.0
+title: echo-chatbot
 app_file: app.py
-pinned: false
+sdk: gradio
+sdk_version: 4.44.1
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import os 
+import torch
+import numpy as np
+import lightning as pl
+import gradio as gr 
+from PIL import Image
+from torchvision import transforms
+from timeit import default_timer as timer 
+from torch.nn import functional as F
+
+torch.set_float32_matmul_precision('medium')
+device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
+torch.set_default_device( device= device  )
+torch.autocast(enabled = True,dtype='float16',device_type='cuda')
+
+pl.seed_everything(123, workers=True)
+
+TEST_TRANSFORMS = transforms.Compose([
+            transforms.Resize((224, 224)),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+        ])
+class_labels= ['Beagle', 'Boxer', 'Bulldog', 'Dachshund', 'German_Shepherd', 'Golden_Retriever','Labrador_Retriever', 'Poodle','Rottweiler','Yorkshire_Terrier']
+
+
+# Model
+model = torch.jit.load('best_model.pt').to(device)
+
+@torch.no_grad()
+def predict_fn(img:Image):
+    start_time = timer()
+    try:    
+        # img = np.array(img)   
+        # print(img)     
+        img = TEST_TRANSFORMS(img).to(device)
+        # print(type(img),img.shape)
+        logits = model(img.unsqueeze(0))
+        probabilities = F.softmax(logits,dim=-1)
+        # print(torch.topk(probabilities,k=2))
+        y_pred = probabilities.argmax(dim=-1).item()
+        confidence = probabilities[0][y_pred].item()
+        predicted_label = class_labels[y_pred]
+        # print(confidence,predicted_label)
+        pred_time = round(timer()-start_time,5)
+        res = {f"Title: {predicted_label}":confidence}
+        return (res,pred_time)
+    except Exception as e:
+        print(f"error:: {e}")
+        gr.Error("An error occured 💥!", duration=5)
+        return ({ f"Title ☠️": 0.0},0.0)
+
+
+
+
+
+gr.Interface(
+        fn=predict_fn,
+        inputs=gr.Image(type='pil'),
+        outputs=[
+            gr.Label(num_top_classes=1, label="Predictions"), # what are the outputs?
+            gr.Number(label="Prediction time (s)")
+        ],
+        examples=[ ['examples/'+i] for i in os.listdir(os.path.join( os.path.dirname(__file__) ,'examples'))],
+        title="Dog Breeds Classifier 🐈",
+        description="CNN-based Architecture for Fast and Accurate DogsBreed Classifier",
+        article="Created by muthukamalan.m ❤️",
+        cache_examples=True,
+        ).launch(share=False,debug=False)
+

best_model.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:32a2bf828456af508eab2d44f0305883e779380bb33a9b90004381c68b64ad51
+size 1024905

gradio_cached_examples/16/indices.csv

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+1
+0

gradio_cached_examples/16/log.csv

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+Predictions,Prediction time (s),flag,username,timestamp
+"{""label"": ""Title \u2620\ufe0f"", ""confidences"": [{""label"": ""Title \u2620\ufe0f"", ""confidence"": 0.0}]}",0.0,,,2024-11-20 16:28:01.033882
+"{""label"": ""Title \u2620\ufe0f"", ""confidences"": [{""label"": ""Title \u2620\ufe0f"", ""confidence"": 0.0}]}",0.0,,,2024-11-20 16:28:04.131286