Create app.py

app.py

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+MODEL_NAME="allenai/MolmoE-1B-0924"
+from transformers import AutoModelForCausalLM
+model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, trust_remote_code=True)    
+from transformers import AutoModelForCausalLM, AutoProcessor, GenerationConfig
+from PIL import Image
+import requests
+
+# load the processor
+processor = AutoProcessor.from_pretrained(
+    'allenai/MolmoE-1B-0924',
+    trust_remote_code=True,
+    torch_dtype='auto',
+    device_map='auto'
+)
+
+# load the model
+model = AutoModelForCausalLM.from_pretrained(
+    'allenai/MolmoE-1B-0924',
+    trust_remote_code=True,
+    torch_dtype='auto',
+    device_map='auto'
+)
+
+# process the image and text
+inputs = processor.process(
+    images=[Image.open(requests.get("https://picsum.photos/id/237/536/354", stream=True).raw)],
+    text="Describe this image."
+)
+
+# move inputs to the correct device and make a batch of size 1
+inputs = {k: v.to(model.device).unsqueeze(0) for k, v in inputs.items()}
+
+# generate output; maximum 200 new tokens; stop generation when <|endoftext|> is generated
+output = model.generate_from_batch(
+    inputs,
+    GenerationConfig(max_new_tokens=200, stop_strings="<|endoftext|>"),
+    tokenizer=processor.tokenizer
+)
+
+# only get generated tokens; decode them to text
+generated_tokens = output[0,inputs['input_ids'].size(1):]
+generated_text = processor.tokenizer.decode(generated_tokens, skip_special_tokens=True)
+
+# print the generated text
+print(generated_text)
+
+# >>> This photograph captures a small black puppy, likely a Labrador or a similar breed,
+#     sitting attentively on a weathered wooden deck. The deck, composed of three...
+
+
+# import cv2
+
+
+# class Solution():
+#     def __init__(self,prompt):
+#         self.prompt= prompt
+#         self.output_dir=None
+        
+#     # read a mp4 file and getting its frame at a particular interval.
+#     def read_frame(self,file,interval=1):
+#         video=cv2.VideoCapture(file)
+#         fps= video.get(cv2.CAP_PROP_FPS)
+
+#         frame_interval= fps*interval# fps= 24 frame/sec and interval = 1 sec so frame interval = 24 frame         
+#         while True:
+#             success, frame=video.read()
+#             if not success:
+#                 break
+
+#             if frame % frame_interval==0:
+#                 # process this frame
+#                 """
+#                 .. to do 
+#                 """
+                
+#     def find(self,input_message):
+        
+        
+                
+            
+
+
+
+    # read a .mp4 file
+    # get a interval N spaced
+    
+    
+