Update src/streamlit_app.py

src/streamlit_app.py

@@ -15,6 +15,7 @@ os.environ['HF_HOME'] = '/tmp/hf_cache'
 # Configure page
 st.set_page_config(
     page_title="CLIP Classifier",
+    page_icon="🔍",
     layout="wide"
 )
 
@@ -43,12 +44,26 @@ def classify_input(model, preprocess, device, input_data, positive_prompts, nega
         text_inputs = clip.tokenize(all_prompts).to(device)
         
         if input_type == "image":
-            # Process image
+            # Process image - Fixed handling of uploaded files
             if isinstance(input_data, str):  # URL
-                response = requests.get(input_data)
+                response = requests.get(input_data, timeout=10)
+                response.raise_for_status()  # Raise an error for bad status codes
                 image = Image.open(io.BytesIO(response.content))
-            else:  # Uploaded file
-                image = Image.open(input_data)
+            else:  # Uploaded file - this is the key fix
+                # For uploaded files, we need to read the bytes and convert to PIL Image
+                if hasattr(input_data, 'read'):
+                    # It's a file-like object (UploadedFile)
+                    image_bytes = input_data.read()
+                    image = Image.open(io.BytesIO(image_bytes))
+                    # Reset file pointer for potential future reads
+                    input_data.seek(0)
+                else:
+                    # If it's already a PIL Image or other format
+                    image = input_data
+            
+            # Convert to RGB if necessary (handles different image modes)
+            if image.mode != 'RGB':
+                image = image.convert('RGB')
             
             image_input = preprocess(image).unsqueeze(0).to(device)
             
@@ -114,15 +129,15 @@ def main():
     
     # Sidebar for configuration
     with st.sidebar:
-        st.header(" Configuration")
+        st.header("⚙️ Configuration")
         
         # Input type selection
         input_type = st.radio("Select input type:", ["Image", "Text"])
         
-        st.header(" Define Prompts")
+        st.header("📝 Define Prompts")
         
         # Positive prompts
-        st.subheader("Positive Prompts")
+        st.subheader("✅ Positive Prompts")
         positive_prompts_text = st.text_area(
             "Enter positive prompts (one per line):",
             value="happy face\nsmiling person\njoyful expression\npositive emotion",
@@ -131,7 +146,7 @@ def main():
         )
         
         # Negative prompts
-        st.subheader("Negative Prompts")
+        st.subheader("❌ Negative Prompts")
         negative_prompts_text = st.text_area(
             "Enter negative prompts (one per line):",
             value="sad face\nangry person\nfrowning expression\nnegative emotion",
@@ -150,7 +165,7 @@ def main():
     col1, col2 = st.columns([1, 1])
     
     with col1:
-        st.header(" Input")
+        st.header("📥 Input")
         
         input_data = None
         
@@ -161,38 +176,59 @@ def main():
             if image_option == "Upload":
                 uploaded_file = st.file_uploader(
                     "Choose an image file",
-                    type=['png', 'jpg', 'jpeg', 'gif', 'bmp']
+                    type=['png', 'jpg', 'jpeg', 'gif', 'bmp', 'webp'],  # Added webp support
+                    help="Upload an image file to classify"
                 )
-                if uploaded_file:
+                if uploaded_file is not None:  # More explicit check
                     input_data = uploaded_file
-                    st.image(uploaded_file, caption="Uploaded Image", use_column_width=True)
+                    try:
+                        # Display the uploaded image
+                        st.image(uploaded_file, caption=f"Uploaded: {uploaded_file.name}", use_column_width=True)
+                        # Show file details
+                        st.info(f"File size: {len(uploaded_file.getvalue())} bytes")
+                    except Exception as e:
+                        st.error(f"Error displaying uploaded image: {e}")
             
             else:  # URL
-                image_url = st.text_input("Enter image URL:")
-                if image_url:
+                image_url = st.text_input("Enter image URL:", placeholder="https://example.com/image.jpg")
+                if image_url.strip():  # Check for non-empty URL
                     try:
-                        response = requests.get(image_url)
-                        image = Image.open(io.BytesIO(response.content))
-                        input_data = image_url
-                        st.image(image, caption="Image from URL", use_column_width=True)
-                    except Exception as e:
+                        # Add basic URL validation
+                        if not image_url.startswith(('http://', 'https://')):
+                            st.warning("Please enter a valid URL starting with http:// or https://")
+                        else:
+                            with st.spinner("Loading image..."):
+                                response = requests.get(image_url, timeout=10)
+                                response.raise_for_status()
+                                image = Image.open(io.BytesIO(response.content))
+                                input_data = image_url
+                                st.image(image, caption="Image from URL", use_column_width=True)
+                    except requests.exceptions.RequestException as e:
                         st.error(f"Error loading image from URL: {e}")
+                    except Exception as e:
+                        st.error(f"Error processing image: {e}")
         
         else:  # Text input
             text_input = st.text_area(
                 "Enter text to classify:",
                 height=150,
-                placeholder="Type your text here..."
+                placeholder="Type your text here...",
+                help="Enter the text you want to classify"
             )
             if text_input.strip():
                 input_data = text_input.strip()
                 st.text_area("Text to classify:", value=text_input, height=100, disabled=True)
     
     with col2:
-        st.header(" Results")
+        st.header("📊 Results")
         
-        if input_data and positive_prompts and negative_prompts:
-            if st.button(" Classify", type="primary", use_container_width=True):
+        # Check if we have all required inputs
+        if not positive_prompts or not negative_prompts:
+            st.warning("⚠️ Please define both positive and negative prompts in the sidebar.")
+        elif not input_data:
+            st.info("📝 Please provide input data to classify.")
+        else:
+            if st.button("🚀 Classify", type="primary", use_container_width=True):
                 with st.spinner("Classifying..."):
                     result = classify_input(
                         model, preprocess, device, input_data, 
@@ -220,7 +256,7 @@ def main():
                         st.metric("Negative Score", f"{result['negative_score']:.3f}")
                     
                     # Detailed breakdown
-                    st.subheader(" Detailed Scores")
+                    st.subheader("📈 Detailed Scores")
                     
                     # Positive prompts scores
                     st.write("**Positive Prompts:**")
@@ -231,15 +267,11 @@ def main():
                     st.write("**Negative Prompts:**")
                     for prompt, score in result['detailed_scores']['negative_prompts']:
                         st.progress(float(score), text=f"{prompt}: {score:.3f}")
-        
-        elif not positive_prompts or not negative_prompts:
-            st.warning(" Please define both positive and negative prompts in the sidebar.")
-        
-        elif not input_data:
-            st.info(" Please provide input data to classify.")
+                else:
+                    st.error("Classification failed. Please check your input and try again.")
     
     # Instructions
-    with st.expander(" How to use this app"):
+    with st.expander("ℹ️ How to use this app"):
         st.markdown("""
         1. **Define Prompts**: In the sidebar, enter your positive and negative prompts (one per line)
         2. **Choose Input Type**: Select whether you want to classify images or text
@@ -252,7 +284,21 @@ def main():
         - **Image classification**: "happy dog, playful pet" vs "aggressive dog, angry animal"
         - **Text sentiment**: "positive review, good experience" vs "negative review, bad experience"
         - **Content moderation**: "safe content, family friendly" vs "inappropriate content, offensive material"
+        
+        **Troubleshooting:**
+        - Make sure uploaded images are in supported formats (PNG, JPG, JPEG, GIF, BMP, WebP)
+        - For URLs, ensure they start with http:// or https://
+        - Check that both positive and negative prompts are defined
         """)
+    
+    # Debug information (can be removed in production)
+    if st.checkbox("Show debug info", help="Check this to see debug information"):
+        st.subheader("Debug Information")
+        st.write(f"Device: {device}")
+        st.write(f"Input type: {input_type}")
+        st.write(f"Input data type: {type(input_data)}")
+        st.write(f"Positive prompts count: {len(positive_prompts) if positive_prompts else 0}")
+        st.write(f"Negative prompts count: {len(negative_prompts) if negative_prompts else 0}")
 
 if __name__ == "__main__":
     main()