Update app.py

app.py

@@ -1,52 +1,15 @@
-pip install --upgrade pip
+from transformers import pipeline
+import numpy
 import torch
-from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
+import matplotlib.pyplot
+import PIL
+from PIL import Image
+import streamlit as st
 
-device = "cuda:0" if torch.cuda.is_available() else "cpu"
-torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+st.write('age detection')
+upload_file = st.file_uploader('choose a image file',type='jpg')
 
-model_id = "openai/whisper-large-v3"
+pipe = pipeline("image-classifcation",model='dima806/facial_age_image_detection')
 
-model = AutoModelForSpeechSeq2Seq.from_pretrained(
-    model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=False, use_safetensors=True
-)
-model.to(device)
-
-processor = AutoProcessor.from_pretrained(model_id)
-
-pipe = pipeline(
-    "automatic-speech-recognition",
-    model=model,
-    tokenizer=processor.tokenizer,
-    feature_extractor=processor.feature_extractor,
-    max_new_tokens=128,
-    chunk_length_s=30,
-    batch_size=16,
-    return_timestamps=True,
-    torch_dtype=torch_dtype,
-    device=device,
-)
-result = pipe("/content/BryanThe_Ideal_Republic.ogg", generate_kwargs={"language": "french"})
-print(result["text"]) # transcritpion
-print(result["chunks"]) # translation
-
-from transformers import RagTokenizer, RagRetriever, RagSequenceForGeneration
-
-
-tokenizer = RagTokenizer.from_pretrained("facebook/rag-token-nq")
-retriever = RagRetriever.from_pretrained("facebook/rag-token-nq", index_name="exact", use_dummy_dataset=True)
-rag_model = RagSequenceForGeneration.from_pretrained("facebook/rag-token-nq", retriever=retriever)
-
-def retrieve_and_generate_response(transcribed_text):
-    # Tokenize the transcribed text
-    input_ids = tokenizer(transcribed_text, return_tensors="pt").input_ids
-
-    # Generate response
-    outputs = rag_model.generate(input_ids)
-    response = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
-
-    return response
-
-response = retrieve_and_generate_response(result["text"])
-print("Response:", response)
+st.write(pipe(st.imagw(Image.open(upload_file)))[0]['label'])