Add comprehensive unit tests for BuddAI confidence scoring and fallback mechanisms

- Implemented tests for confidence scoring logic in `test_buddai_confidence.py` and `test_confidence.py`, covering high and low confidence scenarios, escalation thresholds, and validation scoring penalties.
- Created tests for fallback logging functionality in `test_fallback_logging.py`, ensuring fallback prompts are logged correctly and the `/logs` command retrieves log content.
- Developed tests for fallback prompts in `test_fallback_prompts.py`, verifying that specific prompts are used for different models based on confidence levels.
- Generated detailed test reports for multiple test runs, confirming all tests passed successfully.

tests/buddai_fallback.py

@@ -0,0 +1,66 @@
+import os
+import logging
+
+# Optional import for Google Generative AI
+try:
+    import google.generativeai as genai
+    HAS_GEMINI = True
+except ImportError:
+    HAS_GEMINI = False
+
+class FallbackClient:
+    """
+    Handles escalation to external AI models (Gemini) when local confidence is low.
+    """
+    def __init__(self):
+        self.api_key = os.getenv("GEMINI_API_KEY")
+        self.client = None
+        
+        if self.api_key and HAS_GEMINI:
+            try:
+                genai.configure(api_key=self.api_key)
+                # Using gemini-1.5-flash for speed and efficiency (comparable to Sonnet tier)
+                self.client = genai.GenerativeModel('gemini-1.5-flash')
+            except Exception as e:
+                print(f"⚠️ Failed to initialize Gemini client: {e}")
+        elif not HAS_GEMINI:
+            print("⚠️ Warning: google-generativeai package not installed. Fallback disabled.")
+        elif not self.api_key:
+            # Silent fail if key is missing, just disable fallback
+            pass
+
+    def escalate_to_gemini(self, original_prompt: str, buddai_attempt: str, confidence: int) -> str:
+        """
+        Calls Gemini API to improve upon a low-confidence local attempt.
+        
+        Args:
+            original_prompt (str): The user's original query.
+            buddai_attempt (str): The code generated by the local model.
+            confidence (int): The confidence score (0-100) of the local attempt.
+            
+        Returns:
+            str: The improved solution from Gemini.
+        """
+        if not self.client:
+            return f"⚠️ Fallback unavailable: Gemini client not configured (Confidence: {confidence}%)."
+
+        try:
+            prompt = f"""
+            You are an expert coding assistant acting as a fallback for a local AI model.
+            The local model attempted to answer a request but had low confidence ({confidence}%).
+
+            [USER REQUEST]
+            {original_prompt}
+
+            [LOCAL ATTEMPT (Low Confidence)]
+            {buddai_attempt}
+
+            [TASK]
+            Analyze the request and the local attempt. Provide a corrected, high-quality solution.
+            """
+            
+            response = self.client.generate_content(prompt)
+            return f"✨ **Gemini Fallback (Confidence: {confidence}%)**\n\n{response.text}"
+            
+        except Exception as e:
+            return f"❌ Error calling Gemini API: {str(e)}"