Update app.py

app.py

@@ -12,14 +12,13 @@ from pathlib import Path
 try:
     from together import Together
     import PyPDF2
-    from PIL import Image
     import speech_recognition as sr
     import io
     import subprocess
     import sys
 except ImportError as e:
     print(f"Missing dependency: {e}")
-    print("Install with: pip install together PyPDF2 pillow speechrecognition pyaudio")
+    print("Install with: pip install together PyPDF2 speechrecognition pyaudio")
     sys.exit(1)
 
 class ConversationMemory:
@@ -34,7 +33,7 @@ class ConversationMemory:
         interaction = {
             "timestamp": datetime.now().isoformat(),
             "input_type": input_type,
-            "content": content[:500] + "..." if len(content) > 500 else content,  # Truncate for memory
+            "content": content[:500] + "..." if len(content) > 500 else content,
             "response": response[:1000] + "..." if len(response) > 1000 else response,
             "metadata": metadata or {}
         }
@@ -47,25 +46,22 @@ class ConversationMemory:
         self.session_data = {}
     
     def get_relevant_context(self, query: str, limit: int = 3) -> List[Dict]:
-        # Simple relevance scoring - in production, use embeddings
         relevant = []
         query_lower = query.lower()
         
-        for conv in reversed(self.conversations[-10:]):  # Check last 10 interactions
+        for conv in reversed(self.conversations[-10:]):
             score = 0
             content_lower = conv["content"].lower()
             response_lower = conv["response"].lower()
             
-            # Simple keyword matching
             for word in query_lower.split():
-                if len(word) > 3:  # Skip short words
+                if len(word) > 3:
                     if word in content_lower or word in response_lower:
                         score += 1
             
             if score > 0:
                 relevant.append((score, conv))
         
-        # Sort by relevance and return top results
         relevant.sort(key=lambda x: x[0], reverse=True)
         return [conv for score, conv in relevant[:limit]]
 
@@ -101,33 +97,6 @@ class NexusAI:
         except Exception as e:
             return f"Error reading PDF: {str(e)}"
     
-    def analyze_image(self, image_path: str) -> str:
-        """Analyze image and return description"""
-        try:
-            with Image.open(image_path) as img:
-                # Basic image analysis - in production, use vision models
-                width, height = img.size
-                mode = img.mode
-                format_type = img.format
-                
-                description = f"Image Analysis:\n"
-                description += f"- Dimensions: {width}x{height} pixels\n"
-                description += f"- Color mode: {mode}\n"
-                description += f"- Format: {format_type}\n"
-                
-                # Simple color analysis
-                if mode == "RGB":
-                    # Get dominant colors (simplified)
-                    img_small = img.resize((50, 50))
-                    colors = img_small.getcolors(2500)
-                    if colors:
-                        dominant_color = max(colors, key=lambda x: x[0])[1]
-                        description += f"- Dominant color (RGB): {dominant_color}\n"
-                
-                return description
-        except Exception as e:
-            return f"Error analyzing image: {str(e)}"
-    
     def transcribe_audio(self, audio_path: str) -> str:
         """Transcribe audio to text"""
         try:
@@ -143,12 +112,10 @@ class NexusAI:
         """Execute code safely (basic implementation)"""
         try:
             if language.lower() == "python":
-                # Create a temporary file
                 with tempfile.NamedTemporaryFile(mode='w', suffix='.py', delete=False) as f:
                     f.write(code)
                     temp_file = f.name
                 
-                # Execute with timeout
                 try:
                     result = subprocess.run([sys.executable, temp_file], 
                                           capture_output=True, text=True, timeout=10)
@@ -169,14 +136,12 @@ class NexusAI:
         """Build context messages for the AI model"""
         messages = []
         
-        # Add system message
         system_msg = """You are Nexus AI, a creative multimodal assistant that helps users across different types of content. 
-        You excel at connecting insights across text, documents, images, voice, and code. Always provide helpful, 
+        You excel at connecting insights across text, documents, voice, and code. Always provide helpful, 
         contextual responses that build on previous interactions when relevant."""
         
         messages.append({"role": "system", "content": system_msg})
         
-        # Add relevant conversation history
         relevant_context = self.memory.get_relevant_context(user_input)
         for context in relevant_context:
             messages.append({
@@ -184,7 +149,6 @@ class NexusAI:
                 "content": f"[Previous {context['input_type']} interaction] {context['response'][:200]}..."
             })
         
-        # Build current user message
         current_content = f"Input Type: {input_type}\n\n"
         
         if extracted_content:
@@ -213,7 +177,6 @@ class NexusAI:
             
             ai_response = response.choices[0].message.content
             
-            # Store interaction in memory
             self.memory.add_interaction(
                 input_type=input_type,
                 content=user_input + ("\n" + extracted_content if extracted_content else ""),
@@ -225,7 +188,6 @@ class NexusAI:
         except Exception as e:
             return f"❌ Error generating response: {str(e)}"
 
-# Initialize the AI assistant
 nexus_ai = NexusAI()
 
 def initialize_api_key(api_key: str) -> Tuple[str, str]:
@@ -255,7 +217,6 @@ def process_pdf_input(pdf_file, user_question: str, api_key_status: str) -> str:
     if pdf_file is None:
         return "Please upload a PDF file first!"
     
-    # Extract text from PDF - pdf_file is already a file path string
     extracted_text = nexus_ai.extract_text_from_pdf(pdf_file)
     
     if user_question.strip():
@@ -263,22 +224,6 @@ def process_pdf_input(pdf_file, user_question: str, api_key_status: str) -> str:
     else:
         return nexus_ai.generate_response("Please summarize this document", "pdf", extracted_text)
 
-def process_image_input(image_file, user_question: str, api_key_status: str) -> str:
-    """Process image input with question"""
-    if api_key_status != "success":
-        return "❌ Please initialize your Together AI API key first!"
-    
-    if image_file is None:
-        return "Please upload an image file first!"
-    
-    # Analyze image - image_file is already a file path string
-    image_analysis = nexus_ai.analyze_image(image_file)
-    
-    if user_question.strip():
-        return nexus_ai.generate_response(user_question, "image", image_analysis)
-    else:
-        return nexus_ai.generate_response("What can you tell me about this image?", "image", image_analysis)
-
 def process_audio_input(audio_file, user_question: str, api_key_status: str) -> str:
     """Process audio input with question"""
     if api_key_status != "success":
@@ -287,7 +232,6 @@ def process_audio_input(audio_file, user_question: str, api_key_status: str) ->
     if audio_file is None:
         return "Please upload an audio file first!"
     
-    # Transcribe audio - audio_file is already a file path string
     transcribed_text = nexus_ai.transcribe_audio(audio_file)
     
     if user_question.strip():
@@ -324,7 +268,7 @@ def show_conversation_history() -> str:
         return "No conversation history yet. Start chatting to build your knowledge base!"
     
     history = "## 📚 Recent Conversation History\n\n"
-    for i, conv in enumerate(nexus_ai.memory.conversations[-10:], 1):  # Show last 10
+    for i, conv in enumerate(nexus_ai.memory.conversations[-10:], 1):
         timestamp = datetime.fromisoformat(conv["timestamp"]).strftime("%H:%M:%S")
         history += f"**{i}. [{conv['input_type'].upper()}] {timestamp}**\n"
         history += f"Input: {conv['content'][:100]}{'...' if len(conv['content']) > 100 else ''}\n"
@@ -337,20 +281,17 @@ def clear_conversation_history() -> str:
     nexus_ai.memory.clear_history()
     return "✅ Conversation history has been cleared!"
 
-# Create the Gradio interface
 def create_nexus_interface():
     with gr.Blocks(
         theme=gr.themes.Soft(),
         title="Nexus AI Assistant",
         css="""
-        /* Center the main container */
         .gradio-container {
             max-width: 1400px !important;
             margin: 0 auto !important;
             padding: 20px !important;
         }
         
-        /* API Key section styling */
         .api-key-section {
             background: linear-gradient(135deg, #f5f7fa 0%, #c3cfe2 100%);
             border-radius: 12px;
@@ -360,7 +301,6 @@ def create_nexus_interface():
             border: 1px solid #e1e8ed;
         }
         
-        /* Button styling */
         .primary-button {
             background: linear-gradient(135deg, #667eea 0%, #764ba2 100%) !important;
             border: none !important;
@@ -395,20 +335,17 @@ def create_nexus_interface():
             transition: all 0.3s ease !important;
         }
         
-        /* Tab styling */
         .tab-nav button {
             border-radius: 8px 8px 0 0 !important;
             font-weight: 500 !important;
             padding: 12px 20px !important;
         }
         
-        /* Text area with scrollbar */
         .scrollable-textarea textarea {
             overflow-y: auto !important;
             resize: vertical !important;
         }
         
-        /* Card styling for better visual separation */
         .input-card {
             background: #ffffff;
             border-radius: 10px;
@@ -426,7 +363,6 @@ def create_nexus_interface():
             border: 1px solid #e9ecef;
         }
         
-        /* Header gradient animation */
         .header-gradient {
             background: linear-gradient(135deg, #667eea 0%, #764ba2 50%, #667eea 100%);
             background-size: 200% 200%;
@@ -439,7 +375,6 @@ def create_nexus_interface():
             100% { background-position: 0% 50%; }
         }
         
-        /* Status indicator */
         .status-success {
             border-left: 4px solid #00b894 !important;
             background-color: #d1f2eb !important;
@@ -450,7 +385,6 @@ def create_nexus_interface():
             background-color: #fadbd8 !important;
         }
         
-        /* Responsive design */
         @media (max-width: 768px) {
             .gradio-container {
                 padding: 10px !important;
@@ -459,7 +393,6 @@ def create_nexus_interface():
         """
     ) as app:
         
-        # Header
         gr.HTML("""
         <div class="header-gradient" style="text-align: center; padding: 30px; border-radius: 15px; margin-bottom: 25px;">
             <h1 style="color: white; margin: 0; font-size: 3em; font-weight: 700; text-shadow: 2px 2px 4px rgba(0,0,0,0.3);">
@@ -471,7 +404,6 @@ def create_nexus_interface():
         </div>
         """)
         
-        # API Key Section
         with gr.Group(elem_classes=["api-key-section"]):
             gr.HTML("<h3 style='margin-top: 0; color: #2d3748;'>🔑 API Configuration</h3>")
             with gr.Row():
@@ -496,13 +428,10 @@ def create_nexus_interface():
                 elem_classes=["scrollable-textarea"]
             )
             
-            # Hidden state to track API key status
             api_key_state = gr.State(value="not_initialized")
         
-        # Main Interface Tabs
         with gr.Tabs():
             
-            # Text Chat Tab
             with gr.Tab("💬 Text Chat"):
                 with gr.Row():
                     with gr.Column(scale=1, elem_classes=["input-card"]):
@@ -526,7 +455,6 @@ def create_nexus_interface():
                             elem_classes=["scrollable-textarea"]
                         )
             
-            # PDF Analysis Tab
             with gr.Tab("📄 PDF Analysis"):
                 with gr.Row():
                     with gr.Column(scale=1, elem_classes=["input-card"]):
@@ -554,35 +482,6 @@ def create_nexus_interface():
                             elem_classes=["scrollable-textarea"]
                         )
             
-            # Image Analysis Tab
-            with gr.Tab("🖼️ Image Analysis"):
-                with gr.Row():
-                    with gr.Column(scale=1, elem_classes=["input-card"]):
-                        image_file = gr.Image(
-                            label="Upload Image",
-                            type="filepath"
-                        )
-                        image_question = gr.Textbox(
-                            label="Question about Image (optional)",
-                            placeholder="What would you like to know about this image?",
-                            lines=3,
-                            elem_classes=["scrollable-textarea"]
-                        )
-                        image_btn = gr.Button(
-                            "Analyze Image", 
-                            variant="primary",
-                            elem_classes=["primary-button"]
-                        )
-                    
-                    with gr.Column(scale=1, elem_classes=["output-card"]):
-                        image_output = gr.Textbox(
-                            label="Analysis Result",
-                            lines=12,
-                            interactive=False,
-                            elem_classes=["scrollable-textarea"]
-                        )
-            
-            # Voice Processing Tab
             with gr.Tab("🎤 Voice Processing"):
                 with gr.Row():
                     with gr.Column(scale=1, elem_classes=["input-card"]):
@@ -610,7 +509,6 @@ def create_nexus_interface():
                             elem_classes=["scrollable-textarea"]
                         )
             
-            # Code Executor Tab
             with gr.Tab("⚡ Code Executor"):
                 with gr.Row():
                     with gr.Column(scale=1, elem_classes=["input-card"]):
@@ -646,7 +544,6 @@ def create_nexus_interface():
                             elem_classes=["scrollable-textarea"]
                         )
             
-            # Memory & History Tab
             with gr.Tab("🧠 Memory & History"):
                 with gr.Column(elem_classes=["input-card"]):
                     gr.HTML("<h3 style='margin-top: 0;'>Conversation Memory</h3>")
@@ -673,7 +570,6 @@ def create_nexus_interface():
                         elem_classes=["scrollable-textarea"]
                     )
         
-        # Event handlers
         def update_api_status(api_key):
             message, status = initialize_api_key(api_key)
             if status == "success":
@@ -699,12 +595,6 @@ def create_nexus_interface():
             outputs=[pdf_output]
         )
         
-        image_btn.click(
-            fn=process_image_input,
-            inputs=[image_file, image_question, api_key_state],
-            outputs=[image_output]
-        )
-        
         audio_btn.click(
             fn=process_audio_input,
             inputs=[audio_file, audio_question, api_key_state],
@@ -727,7 +617,6 @@ def create_nexus_interface():
             outputs=[history_output]
         )
         
-        # Footer
         gr.HTML("""
         <div style="text-align: center; padding: 25px; margin-top: 30px; border-top: 2px solid #e9ecef; background: linear-gradient(135deg, #f8f9fa 0%, #e9ecef 100%); border-radius: 10px;">
             <p style="color: #495057; font-size: 1.1em; margin: 0;">
@@ -741,7 +630,6 @@ def create_nexus_interface():
     
     return app
 
-# Launch the application
 if __name__ == "__main__":
     app = create_nexus_interface()
     app.launch(