Upload 2 files

app.py

@@ -0,0 +1,52 @@
+import transformers, accelerate
+
+print(accelerate.__version__)
+print(transformers.__version__)
+
+# Image Captioning
+
+from transformers import AutoProcessor
+from transformers import AutoModelForCausalLM
+import torch
+import streamlit as st
+
+device = "cuda" if torch.cuda.is_available() else "cpu" # Set device to GPU if its available
+
+checkpoint = "microsoft/git-base"
+processor = AutoProcessor.from_pretrained(checkpoint) # We would load a tokenizer for language. Here we load a processor to process images
+
+model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)
+
+
+# Text Search
+st.title("Flower Type Demo")
+st.subheader("Upload an image and See how Chinese qisper works")
+
+upload_file = st.file_uploader('Upload an Image')
+
+if upload_file:
+  test_sample = Image.open(upload_file)
+
+  inputs = processor(images=image, return_tensors="pt").to(device)
+  pixel_values = inputs.pixel_values.to(device)
+
+  generated_ids = model.generate(pixel_values=pixel_values, max_length=50)
+  generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+
+
+  for i in range(10):
+    st.write('New Caption is :')
+    st.write(generated_caption)
+    image = pipe(generated_caption).images[0]
+    
+    display(image)
+
+    print("Model Loading + Inference time = " + str(time.time() - t1) + " seconds")
+
+    st.write("Showing the Image")
+    st.image (image, caption=name, width=None, use_column_width=None, clamp=False, channels='RGB', output_format='auto')
+
+    inputs = processor(images=image, return_tensors="pt").to(device)
+    pixel_values = inputs.pixel_values.to(device)
+    generated_ids = model.generate(pixel_values=pixel_values, max_length=50)
+    generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]

requirements.txt

@@ -0,0 +1,8 @@
+transformers
+datasets 
+evaluate
+jiwer
+accelerate
+diffusers 
+transformers 
+scipy