Update app.py

app.py

@@ -1,189 +1,189 @@
-"""
-Visual Question Answering Streamlit Application
-"""
-
-import logging
-import os
-import sys
-import time
-from datetime import datetime
-
-import streamlit as st
-from PIL import Image
-
-# Configure path to include parent directory
-sys.path.append(os.path.dirname(os.path.abspath(__file__)))
-
-# Configure logging
-log_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "logs")
-os.makedirs(log_dir, exist_ok=True)
-log_file = os.path.join(
-    log_dir, f"vqa_app_{datetime.now().strftime('%Y%m%d_%H%M%S')}.log"
-)
-
-logging.basicConfig(
-    level=logging.INFO,
-    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
-    handlers=[logging.FileHandler(log_file), logging.StreamHandler()],
-)
-logger = logging.getLogger("vqa_app")
-
-# Import modules
-from models import VQAInference
-from utils.image_utils import resize_image
-
-# Global variables
-MODEL_OPTIONS = {"BLIP": "blip", "ViLT": "vilt"}
-
-# Setup directories
-uploads_dir = os.path.join(
-    os.path.dirname(os.path.abspath(__file__)), "static", "uploads"
-)
-os.makedirs(uploads_dir, exist_ok=True)
-
-# Configure page
-st.set_page_config(
-    page_title="Visual Question Answering",
-    page_icon="🔍",
-    layout="wide",
-    initial_sidebar_state="expanded",
-)
-
-
-@st.cache_resource
-def load_model(model_name):
-    """Load the VQA model with caching for better performance"""
-    try:
-        logger.info(f"Loading model: {model_name}")
-        return VQAInference(model_name=model_name)
-    except Exception as e:
-        logger.error(f"Error loading model: {str(e)}")
-        st.error(f"Failed to load model: {str(e)}")
-        return None
-
-
-def process_image_and_question(image_file, question, model_name):
-    """Process the uploaded image and question to generate an answer"""
-    start_time = time.time()
-
-    try:
-        # Load image
-        image = Image.open(image_file).convert("RGB")
-        logger.info(f"Image loaded, size: {image.size}")
-
-        # Resize image
-        image = resize_image(image)
-        logger.info(f"Image resized to: {image.size}")
-
-        # Load model
-        model = load_model(model_name)
-        if model is None:
-            return None
-
-        # Generate answer
-        logger.info(f"Generating answer for question: '{question}'")
-        answer = model.predict(image, question)
-        logger.info(f"Answer generated: '{answer}'")
-
-        # Calculate processing time
-        processing_time = time.time() - start_time
-
-        return {"answer": answer, "processing_time": f"{processing_time:.2f} seconds"}
-    except Exception as e:
-        logger.error(f"Error processing request: {str(e)}", exc_info=True)
-        return None
-
-
-def main():
-    """Main function for Streamlit app"""
-    # Header
-    st.title("Visual Question Answering")
-    st.markdown("Upload an image, ask a question, and get AI-powered answers")
-
-    # Sidebar for model selection
-    st.sidebar.title("Model Options")
-    selected_model_name = st.sidebar.radio(
-        "Choose a model:", options=list(MODEL_OPTIONS.keys()), index=0
-    )
-    model_name = MODEL_OPTIONS[selected_model_name]
-
-    st.sidebar.markdown("---")
-    st.sidebar.markdown("## About the Models")
-    st.sidebar.markdown("**BLIP**: General purpose VQA with free-form answers")
-    st.sidebar.markdown("**ViLT**: Better for yes/no questions and specific categories")
-
-    # Main content - two columns
-    col1, col2 = st.columns([1, 1])
-
-    with col1:
-        st.markdown("### Upload & Ask")
-        uploaded_file = st.file_uploader(
-            "Upload an image:", type=["jpg", "jpeg", "png", "bmp", "gif"]
-        )
-
-        question = st.text_input(
-            "Your question about the image:", placeholder="E.g., What is in this image?"
-        )
-
-        submit_button = st.button(
-            "Get Answer", type="primary", use_container_width=True
-        )
-
-        # Preview uploaded image
-        if uploaded_file is not None:
-            st.markdown("### Image Preview")
-            st.image(uploaded_file, caption="Uploaded Image", use_column_width=True)
-
-    with col2:
-        st.markdown("### AI Answer")
-
-        # Process when submit button is clicked
-        if submit_button and uploaded_file is not None and question:
-            with st.spinner("Generating answer..."):
-                result = process_image_and_question(uploaded_file, question, model_name)
-
-                if result:
-                    st.success("Answer generated successfully!")
-
-                    # Display results
-                    st.markdown("#### Question:")
-                    st.write(question)
-
-                    st.markdown("#### Answer:")
-                    st.markdown(
-                        f"<div style='background-color: #f0f2f6; padding: 20px; border-radius: 5px;'>{result['answer']}</div>",
-                        unsafe_allow_html=True,
-                    )
-
-                    st.markdown("#### Processing Time:")
-                    st.text(result["processing_time"])
-                else:
-                    st.error(
-                        "Failed to generate an answer. Please check the image and question, and try again."
-                    )
-
-        elif not uploaded_file and submit_button:
-            st.warning("Please upload an image first.")
-        elif not question and submit_button:
-            st.warning("Please enter a question about the image.")
-        else:
-            st.info("AI answers will appear here after you submit your question")
-
-    # Information about the application
-    st.markdown("---")
-    st.markdown("### About Visual Question Answering")
-    st.markdown("""
-    This application uses multi-modal AI, combining computer vision and natural language processing 
-    to answer questions about images. Here are some examples of questions you can ask:
-    
-    - **Objects**: "What objects are in this image?"
-    - **Counting**: "How many people are in this image?"
-    - **Colors**: "What color is the car?"
-    - **Actions**: "What is the person doing?"
-    - **Spatial relations**: "What is to the left of the chair?"
-    - **Attributes**: "Is the cat sleeping?"
-    """)
-
-
-if __name__ == "__main__":
-    main()
+"""
+Visual Question Answering Streamlit Application
+"""
+
+import logging
+import os
+import sys
+import time
+from datetime import datetime
+
+import streamlit as st
+from PIL import Image
+
+# Configure path to include parent directory
+sys.path.append(os.path.dirname(os.path.abspath(__file__)))
+
+# Configure logging
+log_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "logs")
+os.makedirs(log_dir, exist_ok=True)
+log_file = os.path.join(
+    log_dir, f"vqa_app_{datetime.now().strftime('%Y%m%d_%H%M%S')}.log"
+)
+
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
+    handlers=[logging.FileHandler(log_file), logging.StreamHandler()],
+)
+logger = logging.getLogger("vqa_app")
+
+# Import modules
+from models import VQAInference
+from utils.image_utils import resize_image
+
+# Global variables
+MODEL_OPTIONS = {"BLIP": "blip", "ViLT": "vilt"}
+
+# Setup directories
+uploads_dir = os.path.join(
+    os.path.dirname(os.path.abspath(__file__)), "static", "uploads"
+)
+os.makedirs(uploads_dir, exist_ok=True)
+
+# Configure page
+st.set_page_config(
+    page_title="Visual Question Answering",
+    page_icon="🔍",
+    layout="wide",
+    initial_sidebar_state="expanded",
+)
+
+
+@st.cache_resource
+def load_model(model_name):
+    """Load the VQA model with caching for better performance"""
+    try:
+        logger.info(f"Loading model: {model_name}")
+        return VQAInference(model_name=model_name)
+    except Exception as e:
+        logger.error(f"Error loading model: {str(e)}")
+        st.error(f"Failed to load model: {str(e)}")
+        return None
+
+
+def process_image_and_question(image_file, question, model_name):
+    """Process the uploaded image and question to generate an answer"""
+    start_time = time.time()
+
+    try:
+        # Load image
+        image = Image.open(image_file).convert("RGB")
+        logger.info(f"Image loaded, size: {image.size}")
+
+        # Resize image
+        image = resize_image(image)
+        logger.info(f"Image resized to: {image.size}")
+
+        # Load model
+        model = load_model(model_name)
+        if model is None:
+            return None
+
+        # Generate answer
+        logger.info(f"Generating answer for question: '{question}'")
+        answer = model.predict(image, question)
+        logger.info(f"Answer generated: '{answer}'")
+
+        # Calculate processing time
+        processing_time = time.time() - start_time
+
+        return {"answer": answer, "processing_time": f"{processing_time:.2f} seconds"}
+    except Exception as e:
+        logger.error(f"Error processing request: {str(e)}", exc_info=True)
+        return None
+
+
+def main():
+    """Main function for Streamlit app"""
+    # Header
+    st.title("Visual Question Answering")
+    st.markdown("Upload an image, ask a question, and get AI-powered answers")
+
+    # Sidebar for model selection
+    st.sidebar.title("Model Options")
+    selected_model_name = st.sidebar.radio(
+        "Choose a model:", options=list(MODEL_OPTIONS.keys()), index=0
+    )
+    model_name = MODEL_OPTIONS[selected_model_name]
+
+    st.sidebar.markdown("---")
+    st.sidebar.markdown("## About the Models")
+    st.sidebar.markdown("**BLIP**: General purpose VQA with free-form answers")
+    st.sidebar.markdown("**ViLT**: Better for yes/no questions and specific categories")
+
+    # Main content - two columns
+    col1, col2 = st.columns([1, 1])
+
+    with col1:
+        st.markdown("### Upload & Ask")
+        uploaded_file = st.file_uploader(
+            "Upload an image:", type=["jpg", "jpeg", "png", "bmp", "gif"]
+        )
+
+        question = st.text_input(
+            "Your question about the image:", placeholder="E.g., What is in this image?"
+        )
+
+        submit_button = st.button(
+            "Get Answer", type="primary", use_container_width=True
+        )
+
+        # Preview uploaded image
+        if uploaded_file is not None:
+            st.markdown("### Image Preview")
+            st.image(uploaded_file, caption="Uploaded Image",use_container_width=True)
+
+    with col2:
+        st.markdown("### AI Answer")
+
+        # Process when submit button is clicked
+        if submit_button and uploaded_file is not None and question:
+            with st.spinner("Generating answer..."):
+                result = process_image_and_question(uploaded_file, question, model_name)
+
+                if result:
+                    st.success("Answer generated successfully!")
+
+                    # Display results
+                    st.markdown("#### Question:")
+                    st.write(question)
+
+                    st.markdown("#### Answer:")
+                    st.markdown(
+                        f"<div style='background-color: #f0f2f6; padding: 20px; border-radius: 5px;'>{result['answer']}</div>",
+                        unsafe_allow_html=True,
+                    )
+
+                    st.markdown("#### Processing Time:")
+                    st.text(result["processing_time"])
+                else:
+                    st.error(
+                        "Failed to generate an answer. Please check the image and question, and try again."
+                    )
+
+        elif not uploaded_file and submit_button:
+            st.warning("Please upload an image first.")
+        elif not question and submit_button:
+            st.warning("Please enter a question about the image.")
+        else:
+            st.info("AI answers will appear here after you submit your question")
+
+    # Information about the application
+    st.markdown("---")
+    st.markdown("### About Visual Question Answering")
+    st.markdown("""
+    This application uses multi-modal AI, combining computer vision and natural language processing 
+    to answer questions about images. Here are some examples of questions you can ask:
+    
+    - **Objects**: "What objects are in this image?"
+    - **Counting**: "How many people are in this image?"
+    - **Colors**: "What color is the car?"
+    - **Actions**: "What is the person doing?"
+    - **Spatial relations**: "What is to the left of the chair?"
+    - **Attributes**: "Is the cat sleeping?"
+    """)
+
+
+if __name__ == "__main__":
+    main()