Create app.py

app.py

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+import os
+import streamlit as st
+from huggingface_hub import login
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from PIL import Image
+import requests
+import torch
+
+# Step 1: Log in to Hugging Face with your access token from secrets
+huggingface_token = os.getenv("HUGGINGFACE_TOKEN")  # Fetch the token from environment
+if huggingface_token:
+    login(token=huggingface_token)  # Authenticate using the token
+else:
+    st.error("Hugging Face token not found. Please set it in the Secrets section.")
+
+# Step 2: Load the model and tokenizer
+model_name = "meta-llama/Llama-3.2-11B-Vision-Instruct"  # Adjust if needed
+try:
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForCausalLM.from_pretrained(model_name)
+    st.success("Model loaded successfully!")
+except Exception as e:
+    st.error(f"Error loading model: {str(e)}")
+
+# Step 3: Create a simple Streamlit app
+def main():
+    st.title("Llama 3.2 11B Vision Model")
+    st.write("Upload an image and enter a prompt to generate output.")
+    
+    # Upload image
+    image_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
+    prompt = st.text_area("Enter your prompt here:")
+    
+    if st.button("Generate Output"):
+        if image_file and prompt:
+            # Load image
+            image = Image.open(image_file)
+            st.image(image, caption="Uploaded Image", use_column_width=True)
+            
+            # Preprocess the image if needed (convert to tensor, etc.)
+            # This depends on how the model expects the image input
+            
+            # Example of converting image to a format suitable for the model
+            # Note: Adjust this part based on your model's requirements.
+            # Here, we're just using a placeholder for the model input.
+            # You might need to resize or normalize the image based on the model's requirements.
+            # For example:
+            # image_tensor = preprocess_image(image)
+
+            try:
+                # Prepare the input for the model
+                inputs = tokenizer(prompt, return_tensors='pt')
+                
+                # Perform inference
+                # Adjust the input format for the model accordingly
+                # Here we assume the model takes a prompt and an image (adjust as necessary)
+                with torch.no_grad():
+                    model_output = model.generate(**inputs)  # Pass image tensor if required
+                
+                # Decode the output
+                output_text = tokenizer.decode(model_output[0], skip_special_tokens=True)
+                st.write("Generated Output:", output_text)
+            except Exception as e:
+                st.error(f"Error during prediction: {str(e)}")
+        else:
+            st.warning("Please upload an image and enter a prompt.")
+
+if __name__ == "__main__":
+    main()