Delete main.py

main.py

@@ -1,48 +0,0 @@
-import os
-import chromadb
-from chromadb.utils import embedding_functions
-from groq import Groq
-
-class AICore:
-    def __init__(self, api_key):
-        self.client = Groq(api_key=api_key)
-        self.chroma_client = chromadb.PersistentClient(path="./chroma_db")
-        self.embed_fn = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="all-MiniLM-L6-v2")
-        self.collection = self.chroma_client.get_or_create_collection(name="aumcore_memory", embedding_function=self.embed_fn)
-
-    def get_response(self, user_input, user_name="Sanjay"):
-        # Purani yaadein fetch karna (Fact-finding only)
-        results = self.collection.query(query_texts=[user_input], n_results=2)
-        memory_context = "\n".join(results['documents'][0]) if results['documents'] else "No past history."
-
-        # Strict Persona Enforcement (No basic replies allowed)
-        system_prompt = f"""
-        Role: Senior Software Architect (AumCore AI).
-        User: {user_name}.
-        Language: Strictly 60% English & 40% Hindi.
-        Rule: If asking for code, provide a high-level Python script with try-except blocks. 
-        Style: Do NOT be generic. Be professional and helpful.
-        
-        Information from Memory: {memory_context}
-        """
-        
-        messages = [
-            {"role": "system", "content": system_prompt},
-            {"role": "user", "content": "colab me gpu check karne ka code do"},
-            {"role": "assistant", "content": f"{user_name} bhai, Colab mein GPU visibility confirm karne ke liye ye standard script use karein.\n\n```python\nimport torch\ntry:\n    status = torch.cuda.is_available()\n    print(f'GPU Available: {{status}}')\n    if status: print(f'Device: {{torch.cuda.get_device_name(0)}}')\nexcept Exception as e:\n    print(f'Error identifying GPU: {{e}}')\n```"},
-            {"role": "user", "content": user_input}
-        ]
-        
-        completion = self.client.chat.completions.create(
-            model="llama-3.3-70b-versatile",
-            messages=messages,
-            temperature=0.1 # Low temperature for consistency
-        )
-        response = completion.choices[0].message.content
-        
-        # Saving new memory
-        self.collection.add(
-            documents=[f"User: {user_input} | AI: {response[:100]}"],
-            ids=[str(os.urandom(4).hex())]
-        )
-        return response