Delete agent.py

agent.py

@@ -1,98 +0,0 @@
-import os
-from dotenv import load_dotenv
-from tavily import TavilyClient
-from cerebras.cloud.sdk import Cerebras
-
-load_dotenv()
-
-SAMPLE_MODEL = "gpt-oss-120b"
-
-class BasicAgent:
-    def __init__(self):
-        print("--- Initializing BasicAgent ---")
-        
-        # 1. Fetch Secrets
-        self.hf_token = os.getenv("HF_TOKEN")
-        self.tavily_key = os.getenv("TAVILY_API_KEY")
-        self.cerebras_key = os.getenv("CEREBRAS_API_KEY")
-        
-        # 2. Check for missing keys and Raise explicit errors
-        if not self.hf_token:
-            raise ValueError("❌ Secret 'HF_TOKEN' is missing. Please add it in Space Settings.")
-        
-        if not self.tavily_key:
-            raise ValueError("❌ Secret 'TAVILY_API_KEY' is missing. Please add it in Space Settings.")
-            
-        if not self.cerebras_key:
-            raise ValueError("❌ Secret 'CEREBRAS_API_KEY' is missing. Please add it in Space Settings.")
-
-        # 3. Initialize Clients
-        try:
-            self.tavily = TavilyClient(api_key=self.tavily_key)
-            self.llm_client = Cerebras(api_key=self.cerebras_key)
-        except Exception as e:
-            raise RuntimeError(f"❌ Failed to initialize external libraries: {e}")
-
-        self.model = os.getenv("LLM_MODEL", SAMPLE_MODEL)
-        print("✅ BasicAgent initialized successfully.")
-    def _truncate_query(self, query: str, max_len: int = 390) -> str:
-        return query[:max_len] + ("..." if len(query) > max_len else "")
-
-
-    def answer(self, question: str, mode="context") -> str:
-        # Truncate BEFORE Tavily call
-        truncated_question = self._truncate_query(question)
-
-        # Use truncated_question for Tavily
-        context = (
-            self.tavily.get_search_context(query=truncated_question)
-            if mode == "context"
-            else self.tavily.qna_search(query=truncated_question)
-        )
-
-        if not context:
-            context = "No context found. Answer based on your knowledge."
-
-        # Use truncated_question for LLM too
-        messages = [
-            {
-                "role": "system", 
-                "content": (
-                    "You are a precise data extraction engine. "
-                    "Your task is to provide ONLY the exact answer to the user's question based on the context. "
-                    "Do not provide explanations, introductory text, or conversational filler. "
-                    "Do not say 'The answer is' or 'Based on the context'. "
-                    "If the answer is a name, number, or date, return JUST that specific value."
-                )
-            },
-            {
-                "role": "user", 
-                "content": f"Context:\n{context}\n\nQuestion: {truncated_question}\n\nExact Answer:"
-            }
-        ]
-
-        comp = self.llm_client.chat.completions.create(
-            model=self.model,
-            messages=messages,
-            temperature=0.0,  # 0.0 makes the model very strict and factual
-            max_tokens=100
-        )
-
-        return comp.choices[0].message.content.strip()
-
-
-    def __call__(self, question: str) -> str:
-        return self.answer(question)
-
-
-if __name__ == "__main__":
-    agent = BasicAgent()
-
-    # Default (context mode)
-    print(agent("Who founded Tesla?"))
-
-    # Quick Q&A mode
-    print(agent("What is the capital of France?", mode="qna"))
-
-    # Return both context and answer
-    print(agent("Explain Burning Man floods", return_context=True))