Create backend.py

backend.py

@@ -0,0 +1,61 @@
+import os
+from huggingface_hub import InferenceClient
+
+# ---- TOKEN ----
+HF_TOKEN = os.getenv("HF_TOKEN")
+if not HF_TOKEN:
+    raise RuntimeError("HF_TOKEN not found. Add it in Space → Settings → Repository secrets")
+
+# ---- MODEL ----
+MODEL_NAME = "mistralai/Mistral-7B-Instruct-v0.2"
+client = InferenceClient(model=MODEL_NAME, token=HF_TOKEN)
+
+
+def run_llm(prompt: str) -> str:
+    """Run prompt using conversational API (supported by this model)."""
+    try:
+        messages = [{"role": "user", "content": prompt}]
+        resp = client.chat_completion(
+            messages=messages,
+            max_tokens=300,
+            temperature=0.7,
+        )
+        return resp.choices[0].message["content"].strip()
+    except Exception as e:
+        # raise clean error (no stacktrace in UI)
+        raise RuntimeError(f"LLM call failed: {e}")
+
+
+def generate_variants(base_prompt: str):
+    meta_prompt = f"""
+Rewrite the following prompt in 5 improved ways:
+1. More specific
+2. More technical
+3. More concise
+4. More creative
+5. Step-by-step style
+
+Return ONLY the rewritten prompts, one per line.
+
+Original prompt:
+{base_prompt}
+"""
+    text = run_llm(meta_prompt)
+
+    variants = []
+    for line in text.split("\n"):
+        line = line.strip("-• ").strip()
+        if line:
+            variants.append(line)
+
+    # Fallback if parsing fails
+    if len(variants) < 3:
+        variants = [
+            f"You are an expert. {base_prompt}",
+            f"Explain step by step: {base_prompt}",
+            f"Give a concise answer: {base_prompt}",
+            f"Provide a technical explanation: {base_prompt}",
+            f"Explain creatively: {base_prompt}",
+        ]
+
+    return variants