initial commit

Dockerfile

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+# Use an official Python runtime as a parent image
+FROM python:3.8-slim
+
+# Set the working directory in the container
+WORKDIR /usr/src/app
+
+# Copy the current directory contents into the container at /usr/src/app
+COPY . .
+
+# Install any needed packages specified in requirements.txt
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Make port 7860 available to the world outside this container
+EXPOSE 7860
+
+# Define environment variable
+ENV NAME World
+
+# Run app.py when the container launches
+CMD ["python", "app.py"]

app.py

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+import gradio as gr
+from transformers import AutoModelForCausalLM, CodeGenTokenizerFast as Tokenizer
+from PIL import Image
+import gc
+from pympler import asizeof 
+
+# Function to clear model from memory
+def clear_model(model):
+    del model
+    gc.collect()
+
+
+def process_image_and_question(image, question):
+    # Placeholder for your image processing and question answering
+    # Replace this with your actual model processing
+    # For example:
+    # enc_image = model.encode_image(image)
+    # answer = model.answer_question(enc_image, question, tokenizer)
+    # return answer
+
+    FinalOutput = ""
+    model_id = "vikhyatk/moondream1"
+
+    if question == "":
+        question = "describe this image?"
+
+    # Check if the model is already loaded
+    try:
+        model
+    except NameError:
+        # clear_model(model) # Example of clearing the model
+        model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True)
+        tokenizer = Tokenizer.from_pretrained(model_id)
+
+    # Assuming you have a correct way to process the image
+    #image = Image.open('/content/_57e22ed5-217c-4004-a279-eeecc18cbd55.jpg') #/content/Bard_Generated_Image (3).jpg') 
+    # This part of the code is incorrect for a standard transformers model
+    enc_image = model.encode_image(image)
+    FinalOutput += model.answer_question(enc_image, "how many people are there? also explain if the image is weird?", tokenizer)
+
+    model_size = asizeof.asizeof(model)
+    tokenizer_size = asizeof.asizeof(tokenizer)
+    FinalOutput += f"\nModel size in RAM: {model_size} bytes, Tokenizer size in RAM: {tokenizer_size} bytes"
+
+    #model load and set-up = 1 min and inference on CPU = 2 min
+    return FinalOutput
+
+# Define Gradio interface
+iface = gr.Interface(fn=process_image_and_question,
+                     inputs=[gr.Image(type="pil"), gr.Textbox(lines=2, placeholder="Ask a question about the image...")],
+                     outputs="text",
+                     title="Image Question Answering",
+                     description="Upload an image and ask a question about it. ( 2 - 3 min response time expected )")
+
+# Launch the interface
+iface.launch()

requirements.txt

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+gradio
+transformers
+torch
+Pillow
+einops 
+timm #dependencies of moondream1 (Trust remote code is on)
+pympler #analyze the memory behavior of Python objects