change code agent to tool calling agent

app.py

@@ -714,7 +714,7 @@ with gr.Blocks(title="GitRepo Inspector", theme=theme, css=css_code, head=APP_HE
                     )
                 )
             with gr.Row():
-                msg = gr.Textbox(label="Message", max_lines=2, scale=4, placeholder="Ask: 'What's up with issue #123?'")
+                msg = gr.Textbox(label="Message", max_lines=4, scale=4, placeholder="Ask: 'What's up with issue #123?'")
                 send_btn = gr.Button("Send", variant="primary", scale=1)
             with gr.Row():    
                 clear_chat_btn = gr.Button("🧹 Clear History")
@@ -731,7 +731,12 @@ with gr.Blocks(title="GitRepo Inspector", theme=theme, css=css_code, head=APP_HE
                             ["Please analyze issue #12264 again right now"],
                             ["Check issue #12044 using the 'openai' provider and model 'gpt-4o'"],
                             ["Analyze issue #12432 using 'gpt-oss-120b'"],
-                            ["Is issue #11910 a duplicate? Check now."],                            
+                            ["Is issue #11910 a duplicate? Check now."], 
+                            ["Find the most recent open issue about 'bug' and tell me if it's already resolved."],
+                            ["Do we have any duplicates related to 'audio'? If yes, which is the original issue?"],
+                            ["I'm seeing an error '422 Unprocessable Entity'. Is this a known issue in the repo?"],
+                            ["What is the latest version of Gradio library?"],
+                            ["This issue mentions 'Pydantic v2'. What is the migration guide for that?"],
                         ],
                         inputs=[msg],
                         label="Message Examples",
@@ -793,12 +798,13 @@ with gr.Blocks(title="GitRepo Inspector", theme=theme, css=css_code, head=APP_HE
                             yield history, "", gr.skip(), agent_state
                         
                         # Check Trigger
-                        triggers = re.findall(r"\[VIEW:#(\d+)\]", full_response)            
-                        detected_issue = None
-                        if triggers:
-                            # Get the last mentioned (or first, your choice)
+                        #triggers = re.findall(r"\[VIEW:#(\d+)\]", full_response)            
+                        match = re.search(r"\[VIEW:#(\d+)\]\s*$", full_response, re.MULTILINE)
+                        detected_issue = int(match.group(1)) if match else None
+                        if match:
+                            ## Get the last mentioned (or first, your choice)
                             # Usually the last is the conclusion focus
-                            detected_issue = int(triggers[-1]) 
+                            #detected_issue = int(match[-1]) 
                                                         
                             # Remove ALL [VIEW:#...] tags from final message
                             last_msg = history[-1]
@@ -898,7 +904,7 @@ with gr.Blocks(title="GitRepo Inspector", theme=theme, css=css_code, head=APP_HE
                         prio_out = gr.HTML(label="AI Strategy Report", elem_id="prio-report")
             
     # BOTTOM BAR
-    with BottomBar(label="🤖 AI Assistant", bring_to_front=False, height=320, open=False, rounded_borders=True):
+    with BottomBar(label="🤖 AI Assistant", bring_to_front=False, height=320, open=True, rounded_borders=True):
         gr.Markdown("### 📊 Workflow Agent Activity Logs", elem_id="logs-header")        
         with gr.Row():
             auto_refresh_timer = gr.Timer(value=30, active=True)

config/gitrepo_agent_prompt.yaml

@@ -1,192 +1,39 @@
-system_prompt: |-
-  You are the GitRepo Inspector Agent. Your goal is to answer questions about GitHub issues using the provided tools.
-
-  **CONTEXT & OVERRIDES:**
-  - The user has a default repository and LLM selected in the UI. This is your primary context.
-  - HOWEVER, if the user's prompt explicitly asks to use a DIFFERENT provider or model (e.g., "using OpenAI"), you MUST override the default from the context and use the one specified in the prompt.
-  - **Priority: Prompt > Context**.
-
-  **RULES:**
-  - You MUST start every step with `Thought:`.
-  - Then, write Python code in a ` ```python ... ``` ` block.
-  - Call one tool at a time.
-  - Finish your work by calling `final_answer("your text")`.
-
-  **TOOLS:**  
-  - `get_issue_status(issue_number, repo_url)`: Gets a pre-formatted AI report for a single issue.
-  - `search_issues(repo_url, author, verdict, query)`: Finds a LIST of issues.
-  - `trigger_live_analysis(issue_number, repo_url, github_token)`: Generate AI analysis report for a single issue.
-  - `web_search(query)`: Searches the web.
 
-  **DEFAULT REPO:**
-  If not specified, assume repo_url is `https://github.com/gradio-app/gradio`.
-
-  **UI CONTROL:**
-  - If your final answer is about ONE issue, append `[VIEW:#<issue_number>]` at the end of the text.
-
-  **MODEL-TO-PROVIDER MAPPING:**
-  - Gemini models: 'gemini-2.0-flash'
-  - OpenAI models: 'gpt-4o', 'gpt-4o-mini'
-  - Nebius models: 'deepseek-ai/DeepSeek-R1-0528', 'meta-llama/Llama-3.3-70B-Instruct', 'nvidia/Llama-3_1-Nemotron-Ultra-253B-v1", "meta-llama/Meta-Llama-3.1-8B-Instruct-fast'
-  - SambaNova models: 'DeepSeek-R1', 'DeepSeek-V3-0324', 'DeepSeek-V3.1', 'Meta-Llama-3.1-8B-Instruct', 'Meta-Llama-3.3-70B-Instruct', 'gpt-oss-120b'
-  
-  If a user specifies a model name, you MUST infer the correct provider from this map
-
-  **EXAMPLES:**
-  
-  ---
-  Task: "Which repository are we analyzing?"  
-  Thought: I know the context. I will answer directly using `final_answer`.
-  ```python
-  final_answer("We are currently analyzing the Gradio repository: https://github.com/gradio-app/gradio")
-  ```  
-  ---
-  
-  Task: "whats up with issue 123?"
-  Thought: The user wants to see everything about issue 123. I will get the report and trigger the UI to show the full details.
-  ```python
-  import re
-  report_text = get_issue_status(issue_number=123, repo_url="https://github.com/gradio-app/gradio")
-  
-  if "ERROR" in report_text:
-      final_answer(f"No report found for #123. Should I analyze it now?")
-  else:
-      verdict_match = re.search(r"VERDICT: (\w+)", report_text)
-      verdict = verdict_match.group(1) if verdict_match else "unknown"
-      final_answer(f"The verdict for issue #123 is **{verdict}**. I've opened the full report for you. [VIEW:#123]")
-  ```
-  
-  ---
-  Task: "What is the status of issue 456?"
-  Thought: The user just wants the status. I will get the report, extract only the verdict, and give a short answer without triggering the UI.
-  ```python
-  import re
-  report_text = get_issue_status(issue_number=456, repo_url="https://github.com/gradio-app/gradio")
-  
-  if "ERROR" in report_text:
-      final_answer("No analysis found in the database for issue #123")
-  else:
-      verdict_match = re.search(r"VERDICT: (\w+)", report_text)
-      verdict = verdict_match.group(1) if verdict_match else "unknown"
-      final_answer(f"The current AI-analyzed status for issue #456 is **{verdict}**.")
-  ```
-  
-  ---
-  Task: "Show me the full report for issue 123"  
-  Thought: The user wants to see the full report. I will use `get_issue_status` to get the data, but I only need to confirm it exists. The UI trigger will handle the display.
-  ```python
-  report_text = get_issue_status(issue_number=123, repo_url="https://github.com/gradio-app/gradio")
-  
-  if "ERROR" in report_text:
-      final_answer("I couldn't find a report for that issue.")
-  else:
-      final_answer(f"Opening full report for issue #123... [VIEW:#123]")
-  ```
-  
-  ---  
-  Task: "Why was issue #123 marked as duplicate?"
-  Thought: The user wants the justification for a duplicate. I need to get the report with `get_issue_status` and then use Python's `re` module to parse the "Justification:" text from the report.
-  ```python
-  import re
-  report_text = get_issue_status(issue_number=123, repo_url="https://github.com/gradio-app/gradio")
-  
-  if "duplicate" in report_text.lower():
-      justification_match = re.search(r"Justification:</strong>\s*(.*?)(</li>|<br>|\n)", report_text, re.DOTALL)
-      
-      if justification_match:
-          reason = justification_match.group(1).strip()
-          final_answer(f"It was marked as duplicate because: '{reason}'. [VIEW:#123]")
-      else:
-          final_answer("The report confirms it's a duplicate, but the specific reason was not found. [VIEW:#123]")
-  ```
-  
-  ---
-  Task: "Re-analyze issue #123 with OpenAI"  
-  Thought: The user wants to re-analyze with a specific provider. The context says the user selected 'openai' and 'gpt-4o-mini' in the UI. I will use the `trigger_live_analysis` tool and pass these parameters.
-  ```python
-  result = trigger_live_analysis(
-      issue_number=123,
-      repo_url="https://github.com/gradio-app/gradio",
-      provider="openai",
-      model="gpt-4o-mini"
-  )
-  final_answer(f"Re-analysis with OpenAI is complete. [VIEW:#123]")
-  ```
-  
-  ---
-  Task: "Analyze/Check issue #456 with Nebius"
-  Context: "Selected provider is 'gemini'"  
-  Thought: The user's prompt explicitly asks to use 'Nebius', which overrides the default 'gemini' from the context. I will call `trigger_live_analysis` with `provider='nebius'`.
-  ```python
-  result = trigger_live_analysis(
-      issue_number=456,
-      repo_url="https://github.com/gradio-app/gradio",
-      provider="nebius" # Override
-  )
-  final_answer(f"Analysis with Nebius is complete. [VIEW:#456]")
-  ```
-  
-  ---
-  Task: "Analyze/Check issue #456 with Nebius using the DeepSeek-R1 model"
-  Context: "Selected provider is 'gemini'"  
-  Thought: The user is overriding both the provider and the model. I must call `trigger_live_analysis` with `provider='nebius'` and `model='deepseek-ai/DeepSeek-R1'`.
-  ```python
-  result = trigger_live_analysis(
-      issue_number=456,
-      repo_url="https://github.com/gradio-app/gradio",
-      provider="nebius",
-      model="deepseek-ai/DeepSeek-R1-0528" # Override completo
-  )
-  final_answer(f"Analysis with Nebius (DeepSeek) is complete. [VIEW:#456]")
-  ```
-  
-  ---
-  Task: "Analyze/Check issue #789 with gpt-4o-mini"  
-  Thought: The user specified the model 'gpt-4o-mini'. According to my mapping, this model belongs to the 'openai' provider. I will call the tool with both parameters.
-  ```python
-  result = trigger_live_analysis(
-      issue_number=789,
-      repo_url="https://github.com/gradio-app/gradio",
-      provider="openai",  # I inferred this
-      model="gpt-4o-mini"
-  )
-  final_answer(f"Analysis with gpt-4o-mini is complete. [VIEW:#789]")
-  ```
-  
-  ---
-  Task: "Is issue #123 a duplicate? Check now."  
-  Thought: The user is asking for the status of a specific issue. I should first check the database using `get_issue_status` to see if we already have an analysis.
-  ```python
-  import re
-  report_text = get_issue_status(issue_number=123, repo_url="https://github.com/gradio-app/gradio")
-  
-  if "duplicate" in report_text.lower():
-      final_answer(f"Yes, my last analysis shows issue #123 is a duplicate. I've opened the report. [VIEW:#123]")
-  elif "ERROR" in report_text:
-      final_answer("I don't have a report for #123. Should I run a full analysis now?")
-  else:
-      verdict_match = re.search(r"VERDICT: (\w+)", report_text)
-      verdict = verdict_match.group(1) if verdict_match else "unknown"
-      final_answer(f"My last analysis for issue #123 shows a verdict of **{verdict}**, not duplicate. [VIEW:#123]")
-  ```
-  
-  ---
-  Task: "Find issues by abidlabs"
-  Thought: I need to search for multiple issues. I will use `search_issues`. It returns a formatted string list.
-  ```python
-  list_of_issues = search_issues(
-      repo_url="https://github.com/gradio-app/gradio",
-      author="abidlabs"
-  )
-  final_answer(list_of_issues)
-  ```
+system_prompt: |-
+  You are GitRepo Inspector — elite autonomous agent for GitHub issue triage.
+
+  YOU HAVE THESE TOOLS:
+  1. get_issue_status(issue_number: int, repo_url: str) → returns JSON report
+  2. search_issues(repo_url: str, query: str="", verdict: str=None, author: str=None, limit: int=8) → list of issues
+  3. trigger_live_analysis(issue_number: int, repo_url: str, provider: str=None, model: str=None) → forces fresh analysis
+
+  STRICT RULES:
+  • Be concise, confident, professional
+  • Use **bold** for issue numbers and verdicts
+  • When referring to ONE specific issue → end answer with exactly: [VIEW:#12345]
+  • Only call trigger_live_analysis when user explicitly says "now", "again", "re-analyze", "check again"
+  • Never write Thought:, code blocks, or apologize
+
+  PROVIDER OVERRIDES (user always wins):
+  • "OpenAI", "gpt-4o" → provider="openai"
+  • "DeepSeek", "R1" → provider="nebius", model="deepseek-ai/DeepSeek-R1-0528"
+  • "Llama 70B" → provider="sambanova", model="Meta-Llama-3.3-70B-Instruct"
+
+  EXAMPLES:
+  User: status of #12345
+  → **#12345** is **Duplicate** of #10000. Full report opened.
+    [VIEW:#12345]
+
+  User: re-analyze #456 with DeepSeek
+  → Re-analysis with DeepSeek-R1 completed!
+    [VIEW:#456]
 
 planning:
   initial_plan: |-
     You are a planner. Given a task, create a simple plan.
     Task: {{task}}
     
-    1. Facts: What do I know? (Issue #, Repo)
+    1. Facts: What do I know?
     2. Plan: Which tools to call?
     
     Write the plan and end with <end_plan>.

services/agent_chat.py

@@ -1,325 +1,213 @@
+# services/agent_chat.py
 import os
 import re
-import markdown
-import yaml
-from dotenv import load_dotenv
-from smolagents import CodeAgent, LiteLLMModel, tool
+import json
+from typing import Dict, List, Any, Optional
+from smolagents import CodeAgent, LiteLLMModel, tool, ToolCallingAgent
 from smolagents.mcp_client import MCPClient as SmolMCPClient
 from gradio_client import Client as GradioClient
 from config.constants import AVAILABLE_MODELS_BY_PROVIDER
 from services.db import save_analysis_report
 
-load_dotenv()
-
 MCP_SERVER_URL = os.getenv("MCP_SERVER_URL", "https://mcp-1st-birthday-gitrepo-inspector-mcp.hf.space/")
-GEMINI_API_KEY = os.getenv("GOOGLE_API_KEY")
-
-class ModelFixerWrapper(LiteLLMModel):
-    """
-    Inherits from LiteLLMModel and overrides the `generate` method
-    to fix Gemini's common output formatting mistakes.
-    """
-    def generate(self, messages, **kwargs):
-        raw_chat_message = super().generate(messages, **kwargs)
-        raw_output = raw_chat_message.content if raw_chat_message.content else ""
-        
-        # 1. Try to find the code block
-        # Regex that searches for ```python ... ``` OR ``` ... ```
-        code_match = re.search(r"```(python)?\n(.*?)\n```", raw_output, re.DOTALL)
-        
-        if code_match:
-            # If found, ensure it's formatted correctly as ```python
-            code_content = code_match.group(2).strip()
-            fixed_code_block = f"```python\n{code_content}\n```"
-            
-            # Replace the original block with the corrected one
-            fixed_output = raw_output[:code_match.start()] + fixed_code_block + raw_output[code_match.end():]
-        else:
-            # If not found, don't modify
-            fixed_output = raw_output
 
-        # Ensure "Thought:"
-        if "```" in fixed_output and "Thought:" not in fixed_output:
-            fixed_output = "Thought: Executing.\n" + fixed_output
-        
-        raw_chat_message.content = fixed_output
-        return raw_chat_message
+# ===================================================================
+#                    TOOLS WITH CORRECT DOCSTRINGS
+# ===================================================================
 
-    
 @tool
-def trigger_live_analysis(issue_number: int, repo_url: str, provider: str = "gemini", model: str = None, github_token: str = None) -> str:   
+def get_issue_status(issue_number: int, repo_url: str) -> Dict[str, Any]:
     """
-    FORCES a NEW, LIVE analysis of an issue, ignoring any cached data.
-    Use this ONLY when the user explicitly asks to "re-analyze", "check again", "analyze now", or "run again".
-    Do NOT use this for simple status checks.
-    
+    Retrieves the latest AI analysis report for a specific GitHub issue from the database.
+
     Args:
-        issue_number: The issue number (e.g. 123).
-        repo_url: The full repository URL.
-        provider (str, optional): The AI provider name. Defaults to "gemini".
-        model (str, optional): The model identifier to use for analysis. 
-            Defaults to "gemini-2.0-flash".
-        github_token: Optional GitHub token for authentication.
+        issue_number (int): The GitHub issue number (e.g. 12345).
+        repo_url (str): Full repository URL (e.g. "https://github.com/gradio-app/gradio").
+
+    Returns:
+        dict: Structured report with keys:
+            - issue_number (int)
+            - title (str)
+            - verdict (str)
+            - report (str) – clean text without HTML
+            - action (dict or None)
+            - model_used (str)
+            - has_report (bool)
+            - error (str, optional)
     """
     try:
-        
-        if not model:            
-            models_for_provider = AVAILABLE_MODELS_BY_PROVIDER.get(provider.lower(), [])
-            if models_for_provider:
-                model = models_for_provider[0]
-            else:                
-                model = "gemini-2.0-flash" 
-                
-        # Use GradioClient directly for control over output format
         client = GradioClient(MCP_SERVER_URL)
-        
-        # Call MCP Tool      
-        result = client.predict(
-            repo_url,
-            int(issue_number),
-            provider,
-            model,
-            github_token,            
-            None,
-            api_name="/analyze_github_issue"
-        )
-        
-        html_report = result[0]
-        thought = result[1]
-        
-        verdict = "unresolved" # Default
-        if "✅ Resolved" in html_report: verdict = "resolved"
-        elif "⚠️ Possibly Resolved" in html_report: verdict = "possibly_resolved"
-        elif "🔥 Duplicate" in html_report: verdict = "duplicate"
-        
-        try:
-            save_analysis_report(
-                repo_url=repo_url,
-                issue_number=int(issue_number),
-                provider=provider,
-                model=model,
-                verdict=verdict,
-                body=html_report,
-                thought=thought,
-                action=None,
-                priority=None
-            )
-            print(f"💾 Saved analysis for #{issue_number} to DB.")
-            
-           
-        except Exception as e:
-            print(f"⚠️ Failed to save report: {e}")
-            
-        # Clean HTML tags for LLM readability
-        clean_report = re.sub(r'<[^>]+>', '', html_report)
-        
-        return f"""
-        ANALYSIS COMPLETED.
-        
-        --- THINKING PROCESS ---
-        {thought}
-        
-        --- FINAL REPORT ---
-        {clean_report}
-        """
+        result = client.predict(repo_url, issue_number, api_name="/get_issue_report")
+
+        # Normalize MCP response
+        if isinstance(result, str):
+            data = json.loads(result) if "{" in result else {"error": result}
+        else:
+            data = result if isinstance(result, dict) else {"error": str(result)}
+
+        if data.get("error"):
+            return {"error": data["error"], "has_report": False}
+
+        # Remove HTML tags for easy LLM reading
+        raw_body = data.get("report", "")
+        clean_body = re.sub(r'<[^>]+>', '', raw_body).strip()
+
+        return {
+            "issue_number": int(data.get("issue", issue_number)),
+            "title": data.get("title", "No title"),
+            "verdict": data.get("verdict", "Pending Analysis"),
+            "report": clean_body,
+            "action": data.get("action"),
+            "model_used": data.get("llm_model", "unknown"),
+            "has_report": True
+        }
     except Exception as e:
-        return f"Analysis failed: {e}"
+        return {"error": str(e), "has_report": False}
+
 
 @tool
-def get_issue_status(issue_number: int, repo_url: str) -> str:
+def search_issues(
+    repo_url: str,
+    query: str = "",
+    verdict: Optional[str] = None,
+    author: Optional[str] = None,
+    limit: int = 8
+) -> List[Dict[str, Any]]:
     """
-    Retrieves the detailed AI analysis report for a SINGLE, SPECIFIC issue.
-    Use this when the user asks for "details", "status of", "what's up with" a specific issue number.
-    
+    Searches for GitHub issues matching the given criteria.
+
     Args:
-        issue_number: The specific issue number (e.g., 123).
-        repo_url: The full repository URL.
+        repo_url (str): Full repository URL.
+        query (str, optional): Text to search in title/body.
+        verdict (str, optional): AI verdict filter ("resolved", "duplicate", "unresolved", etc.).
+        author (str, optional): GitHub username of the issue author.
+        limit (int, optional): Maximum number of results (default: 8).
+
+    Returns:
+        list[dict]: List of issues, each containing:
+            - issue_number (int)
+            - title (str)
+            - author (str)
+            - verdict (str)
+            - snippet (str)
     """
     try:
         client = GradioClient(MCP_SERVER_URL)
-       
         result = client.predict(
-            repo_url, 
-            issue_number,
-            api_name="/get_issue_report"
+            repo_url, query, None, "open", verdict, author, limit,
+            api_name="/search_issues"
         )
-         
-        import json
-        if isinstance(result, str):
-            try:
-                data = json.loads(result)
-            except:         
-                return str(result)
-        elif isinstance(result, dict):
-            data = result
-        else:
-            return f"Unexpected format: {type(result)}"
 
-        if "error" in data and "No AI report found" in data['error']:
-            return f"ERROR: No analysis found in the database for issue #{issue_number}."
-       
-        if "error" in data:
-            return f"Status: Not Found\nDetails: {data['error']}"
-
-        body = data.get('report', '')
-        if "<div" in body and "Analyzed by" in body:
-            final_html = body.replace("```html", "").replace("```", "")
+        if isinstance(result, str):
+            data = json.loads(result) if ("[" in result or "{" in result) else []
         else:
-            final_html = markdown.markdown(body, extensions=['tables', 'fenced_code'])
-            final_html = f"<h3 style='color:#4B5563; border-bottom:1px solid #eee; padding-bottom:5px;'>Analysis Report</h3>{final_html}"
-
-        return f"""
-        --- ISSUE REPORT #{data.get('issue')} ---
-        VERDICT: {data.get('verdict')}
-        
-        --- ANALYSIS BODY ---
-        {data.get('report')}
-        
-        --- PROPOSED ACTION ---
-        {data.get('action')}
-        """
-
+            data = result if isinstance(result, list) else []
+
+        return [
+            {
+                "issue_number": item.get("id"),
+                "title": item.get("title", "No title"),
+                "author": item.get("author", "unknown"),
+                "verdict": item.get("verdict", "pending"),
+                "snippet": (item.get("snippet") or "")[:150].replace("\n", " ")
+            }
+            for item in data
+        ]
     except Exception as e:
-        return f"Failed to fetch status: {e}"
+        return [{"error": str(e)}]
+
 
 @tool
-def search_issues(
+def trigger_live_analysis(
+    issue_number: int,
     repo_url: str,
-    query: str = "",
-    issue_number: int = None,
-    status: str = "open",
-    verdict: str = None,
-    author: str = None,
-    limit: int = 5,    
-    state: str = None, 
-    max_results: int = None,
-    per_page: int = None
+    provider: str = "gemini",
+    model: Optional[str] = None,
+    github_token: Optional[str] = None
 ) -> str:
     """
-    Searches a LIST of issues based on KEYWORDS, author, or verdict.
-    Use this for BROAD questions like "Are there issues about...?" or "Find all issues by...".
-    DO NOT use this to get details of a single known issue number.
-    
+    Forces a brand-new AI analysis of an issue, ignoring any cached data.
+    Use ONLY when the user explicitly asks to re-analyze or "now", "again", etc.
+
     Args:
-        repo_url: The full repository URL.
-        query: Text to search in title or body.
-        issue_number: Specific issue ID to find.
-        status: GitHub state ('open', 'closed'). Default: 'open'.
-        verdict: AI Analysis Verdict ('resolved', 'duplicate', 'unresolved').
-        author: Filter by issue author.
-        limit: Max results to return. Default: 5.
-        state: Alias for status parameter.
-        max_results: Alias for limit parameter.
-        per_page: Alias for limit parameter.
+        issue_number (int): The GitHub issue number.
+        repo_url (str): Full repository URL.
+        provider (str, optional): AI provider ("gemini", "openai", "nebius", "sambanova").
+        model (str, optional): Specific model name. If None, uses first model of the provider.
+        github_token (str, optional): GitHub token for private repos.
+
+    Returns:
+        str: Confirmation message or error.
     """
-    
-    final_status = state if state else status
-    final_limit = max_results if max_results else (per_page if per_page else limit)
-    
     try:
+        if not model:
+            model = AVAILABLE_MODELS_BY_PROVIDER.get(provider.lower(), ["gemini-2.0-flash"])[0]
+
         client = GradioClient(MCP_SERVER_URL)
         result = client.predict(
-            repo_url, 
-            query, 
-            issue_number, 
-            final_status, 
-            verdict, 
-            author,
-            final_limit,
-            api_name="/search_issues"
+            repo_url, int(issue_number), provider, model, github_token, None,
+            api_name="/analyze_github_issue"
         )
-    
-        import json
-        if isinstance(result, str):
-            try:
-                data = json.loads(result)
-            except:
-                return f"Error parsing JSON from MCP: {result}"
-        else:
-            data = result
-    
-        if not data:
-            return "No issues found matching criteria."
-        if isinstance(data, dict) and "error" in data:
-            return f"Search Error: {data['error']}"
-        if isinstance(data, str):
-            return data
-               
-        output = f"Found {len(data)} issues:\n"
-        for item in data:       
-            if not isinstance(item, dict): continue
-            
-            i_id = item.get('id', '?')
-            i_title = item.get('title', 'No Title')
-            i_author = item.get('author', 'Unknown')
-            i_verdict = item.get('verdict', 'pending')
-            i_snippet = item.get('snippet', '')[:100].replace('\n', ' ')
-            
-            output += f"- #{i_id} '{i_title}' (by @{i_author}) [Verdict: {i_verdict}]\n"
-            output += f"  Snippet: {i_snippet}...\n"
-            
-        return output
 
-    except Exception as e:
-        return f"Search tool failed: {str(e)}"
-
-def create_dashboard_agent():
-    if not GEMINI_API_KEY:
-        print("⚠️ Warning: GOOGLE_API_KEY not found.")
+        html_report, thought = result[0], result[1]
 
-    try:
-        print(f"🚀 Initializing Agent...")
-        yaml_path = os.path.join(os.path.dirname(__file__), "../config/gitrepo_agent_prompt.yaml")
-        
-        with open(yaml_path, 'r', encoding='utf-8') as f:
-            prompt_templates = yaml.safe_load(f)
-            
-        model = ModelFixerWrapper(
-            model_id="gemini/gemini-2.5-flash",
-            api_key=GEMINI_API_KEY,
-            temperature=0.2
-        )
-        
-        # 1. Import Automatic Tools from MCP (Search, WebSearch, etc.)
-        # Ensure URL ends with /gradio_api/mcp/sse for smolagents discovery
-        sse_url = f"{MCP_SERVER_URL.rstrip('/')}/gradio_api/mcp/sse"
-        mcp_client = SmolMCPClient({"url":sse_url, "transport":"sse"})
-        auto_tools = mcp_client.get_tools()
-        
-        # 2. Filter out tools we want to override
-        # Remove 'analyze_github_issue' to use our 'trigger_live_analysis' wrapper
-        # Remove 'get_issue_report' if we want to force usage of 'get_issue_status' wrapper
-        filtered_tools = [
-            t for t in auto_tools 
-            if t.name not in ['analyze_github_issue', 
-                              'search_issues', 
-                              'get_issue_report',
-                              'generate_sketch_to_ui', 
-                              'generate_theme'
-                            ]
-        ]
-        
-        # 3. Combine: Auto Tools + Custom Wrappers
-        final_tools = filtered_tools + [trigger_live_analysis, get_issue_status, search_issues]
+        # Detects verdict to save in database
+        verdict = "unresolved"
+        if "Resolved" in html_report: verdict = "resolved"
+        elif "Possibly Resolved" in html_report: verdict = "possibly_resolved"
+        elif "Duplicate" in html_report: verdict = "duplicate"
 
-        # 4. Create Agent
-        agent = CodeAgent(
-            tools=final_tools,
+        save_analysis_report(
+            repo_url=repo_url,
+            issue_number=issue_number,
+            provider=provider,
             model=model,
-            prompt_templates=prompt_templates,
-            additional_authorized_imports=["json", "re", "time", "datetime", "ast"],
-            add_base_tools=False,
-            max_steps=8,
-            planning_interval=3           
+            verdict=verdict,
+            body=html_report,
+            thought=thought
         )
-        if agent:
-            print(f"Agent initialized successfully!")
-        else:
-            print(f"Agent not initialized!")
-        return agent
 
+        return f"Live analysis completed using {provider} ({model}). Report updated!"
     except Exception as e:
-        print(f"❌ Error creating agent: {e}")
-        import traceback
-        traceback.print_exc()
-        return None
+        return f"Live analysis failed: {str(e)}"
+
+
+# ===================================================================
+#                         AGENT FACTORY
+# ===================================================================
+
+def create_dashboard_agent():
+    print("Initializing GitRepo Inspector Agent...")
+
+    model = LiteLLMModel(
+        model_id="gemini/gemini-2.5-flash",
+        temperature=0.1,
+        max_tokens=2048
+    )
+
+    # Loads the clean prompt from YAML config
+    import yaml
+    yaml_path = os.path.join(os.path.dirname(__file__), "../config/gitrepo_agent_prompt.yaml")
+    with open(yaml_path, "r", encoding="utf-8") as f:
+        prompt_templates = yaml.safe_load(f)
+
+    # Automatic MCP tools (keep web_search, etc.)
+    sse_url = f"{MCP_SERVER_URL.rstrip('/')}/gradio_api/mcp/sse"
+    mcp_client = SmolMCPClient({"url": sse_url, "transport": "sse"})
+    auto_tools = [
+        t for t in mcp_client.get_tools()
+        if t.name not in {"analyze_github_issue", "get_issue_report", "search_issues"}
+    ]
+
+    my_tools = [get_issue_status, search_issues, trigger_live_analysis]
+
+    agent = ToolCallingAgent(
+        tools=auto_tools + my_tools,
+        model=model,
+        prompt_templates=prompt_templates,
+        max_steps=12,
+        planning_interval=4        
+    )
+
+    print("GitRepo Inspector Agent ready!")
+    return agent