alternativa

app.py

@@ -1,14 +1,15 @@
 #https://discuss.huggingface.co/t/dynamical-flexible-output/18146/6
 #https://github.com/gradio-app/gradio/issues/2066
 import gradio as gr
+from openai import OpenAI
 import pandas as pd
 from datetime import datetime, timedelta, timezone
-from config import groq_token, groq_model, QUESTION_PROMPT, init_google_sheets_client, groq_model, default_model_name, user_names, google_sheets_name, AtlasClient, custom_model
+from config import groq_token, groq_model, QUESTION_PROMPT, init_google_sheets_client, groq_model, default_model_name, user_names, google_sheets_name, AtlasClient, custom_model, open_ruter_token, openai_model
 import gspread
 from  groq import Client
 import random, string, json, io
 #from trash_folder.alter_app import Local_llm
-from import_model import Local_llm
+from import_model import ModelLoader
 import groq
 import torch
 print(groq.__version__)
@@ -21,7 +22,7 @@ system_prompts_sheet = sheet.worksheet("System Prompts")
 
 # Combine both model dictionaries
 all_models = {}
-all_models.update(groq_model)
+all_models.update(openai_model)
 if torch.cuda.is_available():
     all_models.update(custom_model)
 
@@ -39,7 +40,7 @@ def randomize_key_order(aux):
 alphabet = list(string.ascii_uppercase)
 
 # Initialize GROQ client
-groq_clinet = Client(api_key=groq_token)
+#groq_clinet = Client(api_key=groq_token)
 
 # Load stories from Google Sheets
 def load_stories():
@@ -141,25 +142,21 @@ def save_comment_score(score, comment, story_name, user_name, system_prompt, mod
 
 
 
-from openai import OpenAI
-client = OpenAI(
+clinet = OpenAI(
   base_url="https://openrouter.ai/api/v1",
-  api_key="$OPENROUTER_API_KEY",
+  api_key="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",
 )
-def interact_openrouter(context, model_name):
-    completion = client.chat.completions.create(
-      model=model_name,
-      messages=context,
-    )
-    return completion.choices[0].message.content
-
-
-
+#def interact_openrouter(context, model_name):
+#    completion = client.chat.completions.create(
+#      model=model_name,
+#      messages=context,
+#    )
+#    return completion.choices[0].message.content
 
 
 # Function to handle interaction with model
 def interact_groq(context, model_name):
-   chat_completion = groq_clinet.chat.completions.create(
+   chat_completion = clinet.chat.completions.create(
        messages=context,
        model=model_name,
        temperature=0.1,
@@ -199,7 +196,7 @@ Here is the story:
                         del local_model
                         torch.cuda.empty_cache()
                         torch.cuda.synchronize() #ver si funciona este
-                        local_model = Local_llm(custom_model[model_name])
+                        local_model = ModelLoader(custom_model[model_name])
                     response = local_model.interact(chat_history)
                 else:
                     response = interact_groq(chat_history, model_name)
@@ -489,5 +486,5 @@ with gr.Blocks() as demo:
         inputs=[score_input, comment_input, story_dropdown, user_dropdown, system_prompt_dropdown,  model_checkbox],
         outputs=[data_table, comment_input])
 
-demo.launch(share=True)
+demo.launch(share=False)
 #demo.launch(share=True)

config.py

@@ -8,6 +8,7 @@ hugging_face_token = os.getenv("HUGGING_FACE_TOKEN")
 replicate_token = os.getenv("REPLICATE_TOKEN")
 groq_token = os.getenv("GROQ_TOKEN")
 atlas_token = os.getenv("ATLAS_TOKEN")
+open_ruter_token = os.getenv("OPEN_RUTER_TOKEN")
 
 #atlas configuration
 class AtlasClient: