Create app.py

app.py

@@ -0,0 +1,78 @@
+import torch
+from PIL import Image
+from transformers import AutoModel, AutoTokenizer
+import streamlit as st
+from transformers import pipeline
+from huggingface_hub import InferenceClient
+import os
+
+# Define your API key here
+my_key = "your_api_key_here"
+
+# Load tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-V-2', trust_remote_code=True)
+model = AutoModel.from_pretrained('openbmb/MiniCPM-V-2')
+model.eval()
+
+# Set device for model
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+model = model.to(device=device, dtype=torch.float16 if device == 'cuda' else torch.float32)
+
+# Retrieve the API key from the environment
+api_key = os.getenv("HF_API_KEY")
+
+# Initialize the Hugging Face Inference client with the API key
+client = InferenceClient(api_key=api_key)
+
+# Streamlit UI setup
+st.title("Image Questioning and Content Generation App")
+st.write("Upload an image and ask a question. The model will respond with a description, and you can generate a song or story based on the response.")
+
+# Upload an image
+uploaded_image = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
+if uploaded_image:
+    image = Image.open(uploaded_image).convert('RGB')
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+
+# Text input for the question
+question = st.text_input("Ask a question about the image")
+if question and uploaded_image:
+    msgs = [{'role': 'user', 'content': question}]
+    
+    # Model's response to the image question
+    with st.spinner("Processing..."):
+        res, context, _ = model.chat(
+            image=image,
+            msgs=msgs,
+            context=None,
+            tokenizer=tokenizer,
+            sampling=True,
+            temperature=0.7
+        )
+    
+    st.write("Model's response:", res)
+
+    # Options for generating content based on the response
+    option = st.selectbox("Generate content based on the response", ["Choose...", "Write a Song", "Write a Story"])
+
+    if option != "Choose...":
+        # Create a message based on user choice
+        if option == "Write a Song":
+            messages = [{"role": "user", "content": f"Write a song about the following: {res}"}]
+        elif option == "Write a Story":
+            messages = [{"role": "user", "content": f"Write a story about the following: {res}"}]
+
+        # Stream the content generation
+        st.write(f"Generating {option.lower()}...")
+
+        stream = client.chat.completions.create(
+            model="meta-llama/Llama-3.2-3B-Instruct", 
+            messages=messages, 
+            max_tokens=500,
+            stream=True
+        )
+
+        generated_text = ""
+        for chunk in stream:
+            generated_text += chunk.choices[0].delta.content
+            st.write(generated_text)  # Display each chunk as it's generated