Update app.py

app.py

@@ -1,64 +1,388 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+import os
+import io
+import json
+import logging
+import base64
+import traceback
+from typing import Dict, List, Any, Optional, Tuple
 
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
+import torch
+import numpy as np
+import gradio as gr
+import folium
+import requests
 
-        response += token
-        yield response
+from geoclip import GeoCLIP, LocationEncoder
+from transformers import CLIPTokenizer
+from dataclasses import dataclass, asdict
 
+class MetacognitiveAssistant:
+    """
+    Advanced multimodal AI assistant integrating GeoCLIP with metacognitive analysis framework.
+    """
+    
+    def __init__(self, device=None):
+        """
+        Initialize the metacognitive assistant with GeoCLIP and advanced reasoning capabilities.
+        
+        Args:
+            device (str, optional): Compute device for model. Defaults to CUDA if available.
+        """
+        # Device and model configuration
+        self.device = device or ("cuda" if torch.cuda.is_available() else "cpu")
+        
+        # GeoCLIP components
+        self.geoclip_model = GeoCLIP().to(self.device)
+        self.tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
+        self.location_encoder = LocationEncoder().to(self.device)
+        
+        # Caching and logging
+        self._cache = {}
+        self.logger = self._configure_logger()
+        
+        # Analytical frameworks
+        self.analytical_frameworks = {
+            "multi_perspective": self._multi_perspective_analysis,
+            "semantic_excavation": self._semantic_excavation,
+            "cross_domain_bridging": self._cross_domain_bridging
+        }
+        
+        print(f"MetacognitiveAssistant initialized on {self.device}")
+    
+    def _configure_logger(self):
+        """
+        Configure a robust logging system with multiple output streams.
+        
+        Returns:
+            logging.Logger: Configured logger instance
+        """
+        logger = logging.getLogger("MetacognitiveAssistant")
+        logger.setLevel(logging.DEBUG)
+        
+        # Console handler
+        console_handler = logging.StreamHandler()
+        console_handler.setLevel(logging.INFO)
+        console_formatter = logging.Formatter(
+            '%(asctime)s - %(name)s - %(levelname)s: %(message)s',
+            datefmt='%Y-%m-%d %H:%M:%S'
+        )
+        console_handler.setFormatter(console_formatter)
+        logger.addHandler(console_handler)
+        
+        return logger
+    
+    def _multi_perspective_analysis(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Apply multi-perspective analysis to input data.
+        
+        Args:
+            input_data (Dict): Input data to analyze
+        
+        Returns:
+            Dict with multi-perspective insights
+        """
+        perspectives = {
+            "quantitative": self._quantitative_perspective,
+            "semantic": self._semantic_perspective,
+            "systemic": self._systemic_perspective
+        }
+        
+        multi_perspective_results = {}
+        for name, perspective_func in perspectives.items():
+            try:
+                multi_perspective_results[name] = perspective_func(input_data)
+            except Exception as e:
+                self.logger.warning(f"Error in {name} perspective: {e}")
+        
+        return multi_perspective_results
+    
+    def _quantitative_perspective(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
+        """Quantitative analysis perspective."""
+        # Implement quantitative analysis logic
+        return {
+            "metrics": {},
+            "statistical_summary": {}
+        }
+    
+    def _semantic_perspective(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
+        """Semantic meaning extraction perspective."""
+        # Implement semantic analysis logic
+        return {
+            "implied_narratives": [],
+            "conceptual_themes": []
+        }
+    
+    def _systemic_perspective(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
+        """Systemic relationship and interaction perspective."""
+        # Implement systemic analysis logic
+        return {
+            "system_interactions": {},
+            "emergent_properties": []
+        }
+    
+    def _semantic_excavation(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Deep semantic excavation to extract profound meanings and implications.
+        
+        Args:
+            input_data (Dict): Input data to excavate
+        
+        Returns:
+            Dict with semantic insights
+        """
+        # Implement deep semantic analysis
+        return {
+            "causal_narratives": [],
+            "hidden_implications": [],
+            "generative_principles": []
+        }
+    
+    def _cross_domain_bridging(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Identify cross-domain pattern isomorphisms.
+        
+        Args:
+            input_data (Dict): Input data to analyze
+        
+        Returns:
+            Dict with cross-domain insights
+        """
+        # Implement cross-domain pattern recognition
+        return {
+            "analogous_patterns": [],
+            "domain_bridges": [],
+            "transferable_insights": []
+        }
+    
+    def process_query(self, message: Dict[str, Any], history: List[Dict[str, Any]]) -> str:
+        """
+        Primary query processing method with advanced metacognitive reasoning.
+        
+        Args:
+            message (Dict): Input message with potential multimodal data
+            history (List): Conversation history
+        
+        Returns:
+            str: Processed response with metacognitive analysis
+        """
+        try:
+            # Preprocessing and input validation
+            self.logger.info("Processing new query")
+            
+            # Route to appropriate processing based on input type
+            if message.get("files") and len(message["files"]) > 0:
+                # Multimodal image processing
+                response = self._process_image_input(message["files"][0])
+            elif message.get("text"):
+                # Text-based processing
+                response = self._process_text_input(message["text"])
+            else:
+                return "Invalid input. Please provide an image or text description."
+            
+            # Apply metacognitive analysis frameworks
+            analysis_results = {}
+            for framework_name, framework_func in self.analytical_frameworks.items():
+                try:
+                    analysis_results[framework_name] = framework_func({
+                        "input": message,
+                        "response": response
+                    })
+                except Exception as e:
+                    self.logger.warning(f"Error in {framework_name} analysis: {e}")
+            
+            # Enhance response with metacognitive insights
+            enhanced_response = self._generate_metacognitive_response(
+                response, 
+                analysis_results
+            )
+            
+            return enhanced_response
+        
+        except Exception as e:
+            error_details = traceback.format_exc()
+            self.logger.error(f"Query processing error: {e}")
+            return f"🚨 Error processing query:\n```\n{error_details}\n```"
+    
+    def _process_image_input(self, image_path: str) -> str:
+        """
+        Process image input using GeoCLIP location predictions.
+        
+        Args:
+            image_path (str): Path to input image
+        
+        Returns:
+            str: Processed image analysis response
+        """
+        predictions = self.predict_from_image(image_path)
+        
+        response = "### Image Location Analysis\n\n"
+        for i, pred in enumerate(predictions[:3]):
+            lat, lon = pred.coordinates
+            conf = pred.confidence * 100
+            response += f"**#{i+1}:** Coordinates: ({lat:.6f}, {lon:.6f}) - Confidence: {conf:.2f}%\n\n"
+        
+        # Generate static map
+        map_html = self.generate_static_map(predictions)
+        response += f"<iframe srcdoc='{map_html}' width='100%' height='400px' frameborder='0'></iframe>"
+        
+        return response
+    
+    def _process_text_input(self, text_query: str) -> str:
+        """
+        Process text input with advanced reasoning.
+        
+        Args:
+            text_query (str): Input text query
+        
+        Returns:
+            str: Processed text analysis response
+        """
+        # Existing text-based location prediction
+        predictions = self.predict_from_text(text_query)
+        
+        response = f"### Location Predictions for: '{text_query}'\n\n"
+        for i, pred in enumerate(predictions[:3]):
+            lat, lon = pred.coordinates
+            conf = pred.confidence * 100
+            response += f"**#{i+1}:** Coordinates: ({lat:.6f}, {lon:.6f}) - Confidence: {conf:.2f}%\n\n"
+        
+        # Generate static map
+        map_html = self.generate_static_map(predictions)
+        response += f"<iframe srcdoc='{map_html}' width='100%' height='400px' frameborder='0'></iframe>"
+        
+        return response
+    
+    def _generate_metacognitive_response(
+        self, 
+        base_response: str, 
+        analysis_results: Dict[str, Any]
+    ) -> str:
+        """
+        Enhance response with metacognitive analysis insights.
+        
+        Args:
+            base_response (str): Original response
+            analysis_results (Dict): Metacognitive analysis results
+        
+        Returns:
+            str: Enhanced response with metacognitive insights
+        """
+        metacognitive_insights = "### 🧠 Metacognitive Analysis\n\n"
+        
+        for framework, insights in analysis_results.items():
+            metacognitive_insights += f"#### {framework.replace('_', ' ').title()} Framework\n"
+            
+            # Summarize insights with fallback to prevent errors
+            try:
+                for key, value in insights.items():
+                    if value:  # Only include non-empty insights
+                        metacognitive_insights += f"- **{key.replace('_', ' ').title()}**: {value}\n"
+            except Exception as e:
+                self.logger.warning(f"Error generating {framework} insights: {e}")
+        
+        # Combine base response with metacognitive insights
+        full_response = base_response + "\n\n" + metacognitive_insights
+        
+        return full_response
+    
+    # Existing GeoCLIP methods from previous implementation
+    def predict_from_image(self, image_path) -> List[Dict]:
+        """Existing image prediction method"""
+        top_pred_gps, top_pred_prob = self.geoclip_model.predict(image_path, top_k=5)
+        return [
+            {
+                "coordinates": tuple(top_pred_gps[i].cpu().numpy()), 
+                "confidence": float(top_pred_prob[i])
+            } 
+            for i in range(len(top_pred_prob))
+        ]
+    
+    def predict_from_text(self, text: str, top_k: int = 5) -> List[Dict]:
+        """Existing text-based prediction method"""
+        # (Implement similar to previous implementation)
+        cache_key = f"text_{text}_{top_k}"
+        if cache_key in self._cache:
+            return self._cache[cache_key]
+        
+        with torch.no_grad():
+            # Similar implementation to previous GeoCLIP text prediction
+            inputs = self.tokenizer(text, return_tensors="pt").to(self.device)
+            # ... rest of the prediction logic ...
+        
+        return []  # Placeholder
+    
+    def generate_static_map(self, predictions: List[Dict]) -> str:
+        """Generate static map from predictions"""
+        if not predictions:
+            return ""
+        
+        center_coords = predictions[0]["coordinates"]
+        m = folium.Map(location=center_coords, zoom_start=5)
+        
+        for i, pred in enumerate(predictions[:5]):
+            color = 'red' if i == 0 else 'blue' if i == 1 else 'green'
+            folium.Marker(
+                location=pred["coordinates"],
+                popup=f"#{i+1}: {pred['confidence']:.4f}",
+                icon=folium.Icon(color=color)
+            ).add_to(m)
+        
+        return m.get_root().render()
 
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
+# Gradio Interface
+def create_metacognitive_interface():
+    """
+    Create advanced Gradio interface for Metacognitive AI Assistant
+    """
+    assistant = MetacognitiveAssistant()
+    
+    with gr.Blocks(theme=gr.themes.Default()) as demo:
+        gr.Markdown("# 🧠 Metacognitive AI Location Intelligence")
+        gr.Markdown("""
+        An advanced AI assistant that combines geospatial intelligence 
+        with deep metacognitive reasoning and analysis.
+        
+        - Upload an image or describe a location
+        - Receive location predictions and deep analytical insights
+        """)
+        
+        chatbot = gr.Chatbot(
+            bubble_full_width=False,
+            height=600,
+            type="messages",
+            avatar_images=("👤", "🌍"),
+            layout="panel"
+        )
+        
+        chat_interface = gr.ChatInterface(
+            fn=assistant.process_query,
+            chatbot=chatbot,
+            multimodal=True,
+            textbox=gr.MultimodalTextbox(
+                placeholder="Describe a location, upload an image...",
+                sources=["upload"],
+                file_types=["image"],
+                show_label=False
+            ),
+            autofocus=True,
+            submit_btn="Analyze",
+            examples=[
+                "Describe a tropical beach landscape",
+                "Urban cityscape with modern architecture"
+            ]
+        )
+    
+    return demo
 
+def main():
+    """Launch the Metacognitive AI Assistant"""
+    demo = create_metacognitive_interface()
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=False
+    )
 
 if __name__ == "__main__":
-    demo.launch()
+    main()