initial commit

app.py

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+import streamlit as st
+import pandas as pd
+from io import BytesIO
+from PIL import Image
+import time
+
+from transformers import AutoImageProcessor, ViTForImageClassification
+import torch
+
+image_processor = AutoImageProcessor.from_pretrained("dhanesh123in/image_classification_obipix_birdID")
+model_s = ViTForImageClassification.from_pretrained("dhanesh123in/image_classification_obipix_birdID")
+
+st.title("Welcome to Bird Species Identification App!")
+
+uploaded_file = st.file_uploader("Upload Image")
+if uploaded_file is not None:
+    # To read file as bytes:
+    bytes_data = uploaded_file.getvalue()
+
+    image = Image.open(BytesIO(bytes_data))
+    inputs = image_processor(image, return_tensors="pt")
+    
+    with torch.no_grad():
+        logits = model_s(**inputs).logits
+    
+    # model predicts one of the 1000 ImageNet classes
+    predicted_label = logits.argmax(-1).item()
+    prediction=model_s.config.id2label[predicted_label]
+
+
+    with st.spinner('Our well trained AI assistant is looking at your image...'):
+        time.sleep(5)
+    st.success("Prediction is "+prediction)
+    
+    st.image(bytes_data)
+
+    x=st.radio("Was this correct?",["Yes","No"],horizontal=True)
+
+    if (x=="No"):
+        st.write("Oops.. more to learn I guess")
+
+
+
+    
+
+    

requirements.txt

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+accelerate==0.28.0
+conllu==4.5.3
+elasticsearch==8.11.1
+evaluate==0.4.1
+faiss-cpu==1.7.4
+fire==0.5.0
+fqdn==1.5.1
+git-python==1.0.3
+importlib-resources==6.1.1
+isoduration==20.11.0
+jiwer==3.0.3
+jsonpointer==2.4
+librosa==0.10.1
+matplotlib==3.8.2
+notebook==7.0.6
+nvidia-cuda-nvcc-cu12==12.2.140
+opencv-python==4.8.1.78
+patchify==0.2.3
+pip-chill==1.0.3
+pipfreeze==2.0.3
+py7zr==0.21.0
+pytest==7.4.3
+rouge-score==0.1.2
+sacrebleu==2.3.3
+sentencepiece==0.2.0
+seqeval==1.2.2
+streamlit==1.29.0
+tensorflow==2.15.0.post1
+tensorflow-datasets==4.9.3
+torchaudio==0.12.1+cu113
+torchvision==0.13.1+cu113
+transformers==4.38.2
+uri-template==1.3.0
+webcolors==1.13