Create utils/backend.py

utils/backend.py

@@ -0,0 +1,50 @@
+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."