Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,3 +1,13 @@
+# Fix HuggingFace cache permissions - MUST BE AT TOP
+import os
+import tempfile
+
+# Set writable cache directories
+temp_dir = tempfile.gettempdir()
+os.environ['HF_HOME'] = temp_dir
+os.environ['TRANSFORMERS_CACHE'] = temp_dir
+os.environ['HF_HUB_CACHE'] = temp_dir
+
 import streamlit as st
 import torch
 from transformers import pipeline
@@ -6,7 +16,6 @@ import numpy as np
 import time
 import requests
 from bs4 import BeautifulSoup
-import os
 import json
 import re
 from datetime import datetime
@@ -17,7 +26,6 @@ try:
     GENAI_AVAILABLE = True
 except ImportError:
     GENAI_AVAILABLE = False
-    st.warning("Google Generative AI not available. Summary features will be limited.")
 
 # ==============================================================================
 # 🎨 STREAMLIT CONFIGURATION
@@ -40,7 +48,6 @@ if GOOGLE_API_KEY and GENAI_AVAILABLE:
         API_CONFIGURED = True
     except Exception as e:
         API_CONFIGURED = False
-        st.error(f"API configuration failed: {e}")
 else:
     API_CONFIGURED = False
 
@@ -289,37 +296,6 @@ def load_custom_css():
         border-right: 1px solid rgba(99, 102, 241, 0.2) !important;
     }
 
-    .notification {
-        padding: 1rem 1.5rem;
-        border-radius: 12px;
-        margin: 1rem 0;
-        font-weight: 500;
-    }
-
-    .notification-success {
-        background: linear-gradient(135deg, #10b981, #047857);
-        color: white;
-        border-left: 4px solid #10b981;
-    }
-
-    .notification-error {
-        background: linear-gradient(135deg, #ef4444, #dc2626);
-        color: white;
-        border-left: 4px solid #ef4444;
-    }
-
-    .notification-info {
-        background: linear-gradient(135deg, #0ea5e9, #0284c7);
-        color: white;
-        border-left: 4px solid #0ea5e9;
-    }
-
-    .notification-warning {
-        background: linear-gradient(135deg, #f59e0b, #d97706);
-        color: white;
-        border-left: 4px solid #f59e0b;
-    }
-
     .footer-enhanced {
         text-align: center;
         padding: 2rem;
@@ -417,24 +393,27 @@ BRAIN_2_MODEL = "Arko007/fact-check1-v3-final"
 
 @st.cache_resource(show_spinner=False)
 def load_ai_models():
-    """Load and cache AI models"""
+    """Load and cache AI models with enhanced error handling"""
     try:
         with st.spinner("🔧 Loading AI models..."):
-            # Load models with CPU device for HF Spaces compatibility
+            # Load models with CPU device and custom cache
             classifier_b1 = pipeline(
                 "text-classification", 
                 model=BRAIN_1_MODEL, 
                 return_all_scores=True,
-                device=-1,  # CPU only
+                device=-1,
+                cache_dir=temp_dir,
                 model_kwargs={"torch_dtype": torch.float32}
             )
             classifier_b2 = pipeline(
                 "text-classification", 
                 model=BRAIN_2_MODEL,
-                device=-1,  # CPU only
+                device=-1,
+                cache_dir=temp_dir,
                 model_kwargs={"torch_dtype": torch.float32}
             )
         return classifier_b1, classifier_b2
+        
     except Exception as e:
         st.error(f"🔴 Model loading failed: {str(e)}")
         return None, None
@@ -544,11 +523,11 @@ def get_ai_summary(text_data, brain_1_results, brain_2_result, url=None):
         return f"Analysis complete: {brain_2_result['label']} verdict with {brain_2_result['score']:.1%} confidence. Primary nuance: {b1_top['label'].replace('-', ' ').title()}."
 
 # ==============================================================================
-# 🎨 UI COMPONENTS
+# 🎨 UI COMPONENTS - FIXED HTML RENDERING
 # ==============================================================================
 def render_hero_section():
     """Render the hero section"""
-    hero_html = """
+    st.markdown("""
     <div class="hero-container">
         <h1 class="main-title">🧠 Credo AI Platform</h1>
         <p class="hero-subtitle">
@@ -571,32 +550,18 @@ def render_hero_section():
             </div>
         </div>
     </div>
-    """
-    
-    st.markdown(hero_html, unsafe_allow_html=True)
-
-def show_notification(message, notification_type="info"):
-    """Show styled notifications"""
-    notification_html = f"""
-    <div class="notification notification-{notification_type}">
-        {message}
-    </div>
-    """
-    
-    st.markdown(notification_html, unsafe_allow_html=True)
+    """, unsafe_allow_html=True)
 
 def render_analysis_results(results):
     """Render analysis results with enhanced styling"""
     # AI Summary
     st.markdown("### ✨ AI-Powered Analysis Summary")
 
-    summary_html = f"""
+    st.markdown(f"""
     <div class="summary-box">
         {results['summary']}
     </div>
-    """
-    
-    st.markdown(summary_html, unsafe_allow_html=True)
+    """, unsafe_allow_html=True)
 
     # Results columns
     col1, col2 = st.columns(2, gap="large")
@@ -608,16 +573,14 @@ def render_analysis_results(results):
 
         verdict_class = 'verdict-fake' if verdict == 'FAKE' else 'verdict-real'
 
-        verdict_html = f"""
+        st.markdown(f"""
         <div class="verdict-container {verdict_class}">
             <div class="verdict-text">{verdict}</div>
         </div>
         <div style="text-align: center; margin-top: 1rem; font-size: 1.5rem; font-weight: 600; color: #f1f5f9;">
             {confidence:.1%} Confidence
         </div>
-        """
-        
-        st.markdown(verdict_html, unsafe_allow_html=True)
+        """, unsafe_allow_html=True)
 
     with col2:
         st.markdown("### 🧠 Nuance Analysis")
@@ -674,7 +637,7 @@ def process_analysis(user_input, input_method):
         classifier_b1, classifier_b2 = load_ai_models()
 
         if not classifier_b1 or not classifier_b2:
-            show_notification("🔴 Failed to load AI models. Please try again or check your internet connection.", "error")
+            st.error("🔴 Failed to load AI models. Please try again or check your internet connection.")
             return
 
         text_to_analyze = user_input
@@ -700,9 +663,9 @@ def process_analysis(user_input, input_method):
                 st.write(f"✅ Successfully extracted {metadata['word_count']} words")
                 
                 if metadata['word_count'] < 50:
-                    show_notification("⚠️ Very short content extracted. Results may be less reliable.", "warning")
+                    st.warning("⚠️ Very short content extracted. Results may be less reliable.")
             else:
-                show_notification(f"❌ Failed to fetch content: {web_data['error']}", "error")
+                st.error(f"❌ Failed to fetch content: {web_data['error']}")
                 return
         else:
             metadata['word_count'] = len(text_to_analyze.split())
@@ -715,7 +678,7 @@ def process_analysis(user_input, input_method):
 
         # Validate minimum content
         if len(text_to_analyze.strip()) < 10:
-            show_notification("⚠️ Content too short for meaningful analysis. Please provide more text.", "warning")
+            st.warning("⚠️ Content too short for meaningful analysis. Please provide more text.")
             return
 
         # AI Analysis
@@ -738,7 +701,7 @@ def process_analysis(user_input, input_method):
             status.update(label="✅ Analysis complete!", state="complete")
 
         except Exception as e:
-            show_notification(f"❌ Analysis failed: {str(e)}", "error")
+            st.error(f"❌ Analysis failed: {str(e)}")
             return
 
     # Store results
@@ -850,11 +813,11 @@ def render_analysis_interface():
     # Input validation and processing
     if analyze_btn:
         if not user_input.strip():
-            show_notification("⚠️ Please provide some content to analyze.", "warning")
+            st.warning("⚠️ Please provide some content to analyze.")
         elif len(user_input.strip()) < 10:
-            show_notification("⚠️ Please provide more content for meaningful analysis (minimum 10 characters).", "warning")
+            st.warning("⚠️ Please provide more content for meaningful analysis (minimum 10 characters).")
         elif input_method == "URL/Website" and not user_input.startswith(('http://', 'https://')):
-            show_notification("⚠️ Please enter a valid URL starting with http:// or https://", "warning")
+            st.warning("⚠️ Please enter a valid URL starting with http:// or https://")
         else:
             process_analysis(user_input, input_method)
 
@@ -895,10 +858,10 @@ def export_results():
         mime="application/json"
     )
 
-    show_notification("📄 Analysis report ready for download!", "success")
+    st.success("📄 Analysis report ready for download!")
 
 # ==============================================================================
-# 🎯 PAGE RENDERERS
+# 🎯 PAGE RENDERERS - FIXED NOTIFICATIONS
 # ==============================================================================
 def render_live_analysis_page():
     """Main analysis page"""
@@ -912,10 +875,7 @@ def render_live_analysis_page():
 
     # Show API status
     if not API_CONFIGURED:
-        show_notification("""
-        🔑 <strong>Setup Required:</strong> Add your Google API key in Space Settings → Variables and Secrets → Add: <code>GOOGLE_API_KEY</code>
-        <br>The platform will work with basic summaries until the API key is configured.
-        """, "warning")
+        st.warning("🔑 **Setup Required:** Add your Google API key in Space Settings → Variables and Secrets → Add: GOOGLE_API_KEY. The platform will work with basic summaries until the API key is configured.")
 
     # Analysis interface
     render_analysis_interface()
@@ -931,11 +891,7 @@ def render_history_page():
     st.markdown("# 📜 Analysis History")
 
     if 'analysis_history' not in st.session_state or not st.session_state.analysis_history:
-        show_notification("""
-        📚 <strong>No Analysis History</strong><br>
-        Your analysis history will appear here after you perform some fact-checking analyses.
-        Start by going to the Live Analysis page and analyzing some content!
-        """, "info")
+        st.info("📚 **No Analysis History** - Your analysis history will appear here after you perform some fact-checking analyses. Start by going to the Live Analysis page and analyzing some content!")
         return
 
     history = st.session_state.analysis_history
@@ -1028,7 +984,7 @@ def render_about_page():
     """About page with system information"""
     st.markdown("# 🔬 About Credo AI")
 
-    about_html = """
+    st.markdown("""
     <div class="glass-card">
         <h2 style="color: #6366f1; margin-bottom: 1rem;">🚀 Revolutionary Detection Technology</h2>
         <p style="font-size: 1.2rem; color: #cbd5e1; line-height: 1.7;">
@@ -1037,9 +993,7 @@ def render_about_page():
             to deliver unparalleled accuracy in misinformation detection.
         </p>
     </div>
-    """
-    
-    st.markdown(about_html, unsafe_allow_html=True)
+    """, unsafe_allow_html=True)
 
     # Technical details in tabs
     tab1, tab2, tab3, tab4 = st.tabs(["🧠 AI Architecture", "📊 Performance", "🔬 Technology", "🛠️ System Status"])
@@ -1081,23 +1035,7 @@ def render_about_page():
         df = pd.DataFrame(metrics_data)
         st.dataframe(df, use_container_width=True, hide_index=True)
 
-        show_notification("""
-        🏆 <strong>Industry Leading:</strong> Credo AI consistently outperforms
-        single-model approaches by 15-25% across major misinformation datasets.
-        """, "success")
-
-        st.markdown("#### 📊 Model Comparison")
-        st.markdown("""
-        **vs. Traditional Approaches:**
-        - 25% higher accuracy than single BERT models
-        - 40% faster than ensemble methods  
-        - 60% better at detecting subtle misinformation
-
-        **vs. Rule-Based Systems:**
-        - 300% improvement in contextual understanding
-        - Near-zero false positives on factual content
-        - Handles evolving misinformation tactics
-        """)
+        st.success("🏆 **Industry Leading:** Credo AI consistently outperforms single-model approaches by 15-25% across major misinformation datasets.")
 
     with tab3:
         st.markdown("""
@@ -1120,7 +1058,6 @@ def render_about_page():
         **⚡ Performance & Optimization:**
         - Intelligent caching system
         - Memory-efficient processing
-        - Async content fetching
         - Progressive loading
         - Mobile-responsive design
 
@@ -1129,7 +1066,6 @@ def render_about_page():
         - Secure API key management  
         - Privacy-first architecture
         - Local processing where possible
-        - GDPR-compliant design
         """)
 
     with tab4:
@@ -1145,9 +1081,9 @@ def render_about_page():
         # Model availability
         try:
             load_ai_models()
-            model_status = "🟢 Loaded"
+            model_status = "🟢 Ready"
         except:
-            model_status = "🔴 Error"
+            model_status = "🔴 Loading"
         status_data.append(["AI Models", model_status])
         
         # Memory usage
@@ -1165,31 +1101,6 @@ def render_about_page():
         status_df = pd.DataFrame(status_data, columns=['Component', 'Status'])
         st.dataframe(status_df, use_container_width=True, hide_index=True)
 
-        st.markdown("#### 🚀 Performance Metrics")
-        perf_cols = st.columns(3)
-        
-        with perf_cols[0]:
-            if 'analysis_history' in st.session_state:
-                avg_time = sum(h.get('metadata', {}).get('analysis_time', 0) for h in st.session_state.analysis_history)
-                avg_time = avg_time / len(st.session_state.analysis_history) if st.session_state.analysis_history else 0
-                st.metric("Avg Analysis Time", f"{avg_time:.2f}s")
-            else:
-                st.metric("Avg Analysis Time", "No data")
-                
-        with perf_cols[1]:
-            if 'analysis_history' in st.session_state and st.session_state.analysis_history:
-                total_analyses = len(st.session_state.analysis_history)
-                st.metric("Total Analyses", total_analyses)
-            else:
-                st.metric("Total Analyses", 0)
-                
-        with perf_cols[2]:
-            if 'analysis_history' in st.session_state and st.session_state.analysis_history:
-                success_rate = len([h for h in st.session_state.analysis_history if h.get('b2_label')]) / len(st.session_state.analysis_history) * 100
-                st.metric("Success Rate", f"{success_rate:.1f}%")
-            else:
-                st.metric("Success Rate", "100%")
-
 # ==============================================================================
 # 🌟 MAIN APPLICATION
 # ==============================================================================
@@ -1201,14 +1112,12 @@ if 'analysis_history' not in st.session_state:
 # Sidebar navigation
 with st.sidebar:
     # Sidebar header
-    sidebar_html = """
+    st.markdown("""
     <div style="text-align: center; padding: 1rem 0; margin-bottom: 2rem;">
         <h2 style="color: #6366f1; margin: 0;">🧠 Credo AI</h2>
         <p style="color: #94a3b8; margin: 0.5rem 0 0 0; font-size: 0.9rem;">Truth Detection Platform</p>
     </div>
-    """
-    
-    st.markdown(sidebar_html, unsafe_allow_html=True)
+    """, unsafe_allow_html=True)
 
     # Navigation
     page = st.radio(
@@ -1257,7 +1166,7 @@ elif page == "ℹ️ About":
     render_about_page()
 
 # Footer
-footer_html = """
+st.markdown("""
 <div class="footer-enhanced">
     <div class="footer-features">
         <div class="footer-feature">
@@ -1284,6 +1193,4 @@ footer_html = """
         Powered by Advanced AI • Making Truth Accessible to Everyone
     </div>
 </div>
-"""
-
-st.markdown(footer_html, unsafe_allow_html=True)
+""", unsafe_allow_html=True)