Delete backend.py

backend.py

@@ -1,50 +0,0 @@
-import os
-
-# Default: Hugging Face or Ollama fallback
-def run_llm(prompt: str) -> str:
-    """
-    Route prompt to an available LLM backend.
-    - Prefers Hugging Face Inference API if HF_API_TOKEN is set
-    - Falls back to Ollama (local) if OLLAMA_MODEL is set
-    - Otherwise returns a static placeholder response
-    """
-    # Hugging Face API
-    hf_token = os.getenv("HF_API_TOKEN")
-    hf_model = os.getenv("HF_MODEL", "mistralai/Mistral-7B-Instruct-v0.3")
-    if hf_token:
-        try:
-            import requests
-            response = requests.post(
-                f"https://api-inference.huggingface.co/models/{hf_model}",
-                headers={"Authorization": f"Bearer {hf_token}"},
-                json={"inputs": prompt},
-                timeout=30
-            )
-            if response.status_code == 200:
-                return response.json()[0]["generated_text"]
-            else:
-                return f"⚠️ HF error {response.status_code}: {response.text}"
-        except Exception as e:
-            return f"⚠️ HF backend error: {e}"
-
-    # Ollama API
-    ollama_model = os.getenv("OLLAMA_MODEL", "llama2")
-    try:
-        import requests
-        response = requests.post(
-            "http://localhost:11434/api/generate",
-            json={"model": ollama_model, "prompt": prompt},
-            timeout=30,
-        )
-        text = ""
-        for line in response.iter_lines():
-            if line:
-                chunk = line.decode("utf-8")
-                if '"response":"' in chunk:
-                    text += chunk.split('"response":"')[1].split('"')[0]
-        return text if text else "⚠️ Ollama returned no output"
-    except Exception:
-        pass
-
-    # Fallback static
-    return "⚠️ No LLM backend configured. Please set HF_API_TOKEN or run Ollama."