Update app.py

app.py

@@ -7,14 +7,23 @@ from dotenv import load_dotenv
 import re
 import huggingface_hub
 import spaces
-import httpx
 
 load_dotenv()
 
 app = FastAPI()
 
 global_data = {
-    'models': []
+    'models': [],
+    'tokens': {
+        'eos': 'eos_token',
+        'pad': 'pad_token',
+        'padding': 'padding_token',
+        'unk': 'unk_token',
+        'bos': 'bos_token',
+        'sep': 'sep_token',
+        'cls': 'cls_token',
+        'mask': 'mask_token'
+    }
 }
 
 model_configs = [
@@ -48,7 +57,6 @@ class ModelManager:
     def load_model(self, model_config):
         return {"model": Llama.from_pretrained(repo_id=model_config['repo_id'], filename=model_config['filename']), "name": model_config['name']}
     
-    @spaces.GPU(duration=0)
     def load_all_models(self):
         if self.loaded:
             return self.models
@@ -109,22 +117,18 @@ def remove_repetitive_responses(responses):
     for response in responses:
         normalized_response = remove_duplicates(response['response'])
         if normalized_response not in seen:
-            seen.add(normalized_response)
             unique_responses.append(response)
+            seen.add(normalized_response)
     return unique_responses
 
 def select_best_response(responses):
+    if not responses:
+        return ""
     responses = remove_repetitive_responses(responses)
-    responses = [remove_duplicates(response['response']) for response in responses]
-    unique_responses = list(dict.fromkeys(responses))
-    sorted_responses = sorted(unique_responses, key=lambda r: len(r), reverse=True)
-    return sorted_responses[0]
+    return max(set(responses), key=lambda x: x['response'].count("user"))
 
-@app.post("/generate_chat")
-async def generate_chat(request: ChatRequest):
-    if not request.message.strip():
-        raise HTTPException(status_code=400, detail="Error: No message provided.")
-    
+@app.post("/generate")
+def generate_chat(request: ChatRequest):
     responses = []
     num_models = len(global_data['models'])