Upload backend.py

backend.py

@@ -0,0 +1,97 @@
+import torch
+from huggingface_hub import HfApi
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import pandas as pd
+import re
+
+class ModelResearcher:
+    def __init__(self):
+        self.api = HfApi()
+
+    def search_models(self, task_domain="Language", architecture_type="All", sort_by="downloads", limit=50):
+        hf_task = "text-generation" if task_domain == "Language" else "image-classification"
+        filter_tags = []
+        if architecture_type == "Recurrent (RNN/RWKV/Mamba)": filter_tags.append("rwkv") 
+        elif architecture_type == "Attention (Transformer)": filter_tags.append("transformers")
+        
+        models = self.api.list_models(
+            sort=sort_by, direction=-1, limit=limit,
+            filter=filter_tags if filter_tags else None, task=hf_task
+        )
+        
+        model_list = []
+        for m in models:
+            size_match = re.search(r'([0-9\.]+)b', m.modelId.lower())
+            size_label = f"{size_match.group(1)}B" if size_match else "N/A"
+            if size_label == "N/A": # Fallback check for millions
+                 size_match_m = re.search(r'([0-9\.]+)m', m.modelId.lower())
+                 size_label = f"{size_match_m.group(1)}M" if size_match_m else "N/A"
+
+            model_list.append({
+                "model_id": m.modelId, "likes": m.likes, "downloads": m.downloads,
+                "created_at": str(m.created_at)[:10], "estimated_params": size_label
+            })
+        return pd.DataFrame(model_list)
+
+class ModelManager:
+    def __init__(self, device="cpu"):
+        self.device = device
+        self.loaded_models = {} 
+
+    def load_model(self, model_id, quantization="None"):
+        """
+        Loads model with optional 8-bit quantization.
+        quantization: "None" (FP16/32) or "8-bit"
+        """
+        # Create a unique key for caching (e.g., "distilgpt2_8bit")
+        cache_key = f"{model_id}_{quantization}"
+        
+        if cache_key in self.loaded_models:
+            return True, "Already Loaded"
+        
+        try:
+            tokenizer = AutoTokenizer.from_pretrained(model_id)
+            if tokenizer.pad_token is None: tokenizer.pad_token = tokenizer.eos_token
+            
+            # Quantization Logic
+            load_kwargs = {"trust_remote_code": True}
+            
+            if quantization == "8-bit":
+                if self.device == "cpu":
+                    return False, "8-bit quantization requires a GPU (CUDA)."
+                load_kwargs["load_in_8bit"] = True
+                load_kwargs["device_map"] = "auto" # Required for bitsandbytes
+            else:
+                # Standard Loading
+                dtype = torch.float16 if self.device == "cuda" else torch.float32
+                load_kwargs["torch_dtype"] = dtype
+                
+            model = AutoModelForCausalLM.from_pretrained(model_id, **load_kwargs)
+            
+            if quantization != "8-bit":
+                model = model.to(self.device)
+            
+            model.eval()
+            self.loaded_models[cache_key] = {"model": model, "tokenizer": tokenizer}
+            return True, "Success"
+        except Exception as e:
+            return False, str(e)
+
+    def generate_text(self, model_id, quantization, prompt, max_new_tokens=100):
+        cache_key = f"{model_id}_{quantization}"
+        if cache_key not in self.loaded_models: return "Error: Model not loaded."
+        
+        pkg = self.loaded_models[cache_key]
+        inputs = pkg["tokenizer"](prompt, return_tensors="pt").to(self.device)
+        
+        with torch.no_grad():
+            outputs = pkg["model"].generate(
+                **inputs, max_new_tokens=max_new_tokens, pad_token_id=pkg["tokenizer"].eos_token_id
+            )
+        return pkg["tokenizer"].decode(outputs[0], skip_special_tokens=True)
+
+    def get_components(self, model_id, quantization="None"):
+        cache_key = f"{model_id}_{quantization}"
+        if cache_key in self.loaded_models:
+            return self.loaded_models[cache_key]["model"], self.loaded_models[cache_key]["tokenizer"]
+        return None, None