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agent.py

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+from pydantic_ai import Agent
+from pydantic_ai.models.ollama import OllamaModel
+from pydantic_ai.providers.ollama import OllamaProvider
+from dotenv import load_dotenv
+import os
+import json
+from prompts import OPPORTUNITY_PROMPT, EVENT_PROMPT
+
+load_dotenv()
+
+ollama_url = os.getenv("OLLAMA_BASE_URL")
+
+prov = OllamaProvider(base_url=ollama_url)
+
+# Use qwen2.5:3b or phi4-mini for low-end hardware (RAM < 8GB)
+model = OllamaModel(
+    model_name='granite4.1:8b',
+    provider=prov
+)
+
+# --- OPPORTUNITY AGENT ---
+opportunity_agent = Agent(
+    model,
+    output_type=str,
+    system_prompt=OPPORTUNITY_PROMPT,
+    retries=5
+)
+
+# --- EVENT AGENT ---
+event_agent = Agent(
+    model,
+    output_type=str,
+    system_prompt=EVENT_PROMPT,
+    retries=5
+)
+
+async def parse_page(content: str, entry_type: str = "opportunity"):
+    """
+    Parse content and extract entry data based on type.
+    
+    Args:
+        content: The raw text content to parse
+        entry_type: Either 'opportunity' or 'event'
+    """
+    # Select the appropriate agent
+    agent = opportunity_agent if entry_type == "opportunity" else event_agent
+    
+    # 1. Run the agent (which returns a string)
+    print(f"Parsing {entry_type}...")
+    print(content)
+    result = await agent.run(content)
+    raw_text = result.output
+    print(raw_text) 
+    
+    # 2. Clean the string
+    # We remove the markdown decorators so json.loads doesn't crash
+    clean_json = raw_text.replace("```json", "").replace("```", "").strip()
+    
+    try:
+        # 3. Convert string to a dictionary
+        data_dict = json.loads(clean_json)
+        
+        # 4. Success! return the dictionary to main.py
+        return data_dict
+        
+    except json.JSONDecodeError as e:
+        print(f"Critical Error: The AI sent invalid JSON. Text was: {raw_text}")
+        raise e