Create app.py

app.py

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+import streamlit as st
+import torch
+from PIL import Image
+from qwen_vl_utils import process_vision_info
+from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
+import time
+
+
+@st.cache_resource
+def load_model():
+    model = Qwen2VLForConditionalGeneration.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", trust_remote_code=True, torch_dtype=torch.float32).eval()
+    processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", trust_remote_code=True)
+    return model, processor
+
+model, processor = load_model()
+
+
+st.title("Image Query App")
+
+
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+
+st.sidebar.title("Suggested Questions")
+predefined_questions = [
+    "What is the main object in this image?",
+    "Describe the scene in the image.",
+    "Are there any people in the image?",
+    "What is the background of the image?"
+]
+selected_question = st.sidebar.radio("Choose a question", predefined_questions)
+
+
+question = st.sidebar.text_input("Or ask your own question here:")
+
+
+submit_button = st.sidebar.button("Submit")
+
+
+response = ""
+
+if uploaded_file is not None:
+    image = Image.open(uploaded_file)
+    
+
+    original_size = image.size
+    st.write(f"Original image dimensions: {original_size}")
+
+
+    max_size = (700, 700)
+    if image.size[0] > 1000 or image.size[1] > 1000:
+        image.thumbnail(max_size)
+        resized_size = image.size
+        st.write(f"Image resized to: {resized_size}")
+    else:
+        st.write("Image size is within acceptable limits.")
+
+    if not question:
+        question = selected_question
+
+
+    if submit_button:
+        st.sidebar.markdown("<h3 style='color:blue;'>Fetching the answer might take 2-3 minutes depending on the question, hold tight while we process your request!</h3>", unsafe_allow_html=True)
+        start_time = time.time()  # Start the timer
+        
+        if question:
+            
+            messages = [
+                {
+                    "role": "user",
+                    "content": [
+                        {"type": "image", "image": image},
+                        {"type": "text", "text": question},
+                    ],
+                }
+            ]
+
+            
+            text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+            image_inputs, video_inputs = process_vision_info(messages)
+            inputs = processor(
+                text=[text],
+                images=image_inputs,
+                videos=video_inputs,
+                padding=True,
+                return_tensors="pt",
+            )
+
+            
+            with st.spinner('Fetching the answer...'):
+                with torch.no_grad():
+                    new_generated_ids = model.generate(**inputs, max_new_tokens=180)
+
+            
+            new_generated_ids_trimmed = [
+                out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, new_generated_ids)
+            ]
+            response = processor.batch_decode(
+                new_generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+            )[0]  
+
+        else:
+            st.warning("Please enter a question.")
+
+        elapsed_time = time.time() - start_time  # Calculate elapsed time
+
+
+if response:
+    st.markdown(f"<h4 style='color:green;'>Response:</h4><p style='font-size:18px;'>{response}</p>", unsafe_allow_html=True)
+    st.markdown(f"<p style='color:gray;'>Time taken to fetch the answer: {elapsed_time:.2f} seconds</p>", unsafe_allow_html=True)
+
+
+if uploaded_file is not None:
+    st.image(image, caption='Uploaded Image', use_column_width=True)