Update app.py

app.py

@@ -3,51 +3,9 @@ import transformers
 import torch
 from peft import PeftModel
 import os
-import csv
-import huggingface_hub
-from huggingface_hub import Repository, hf_hub_download, upload_file
-from datetime import datetime
 
-DATASET_REPO_URL = "https://huggingface.co/datasets/JerniganLab/chat-data"
-DATASET_REPO_ID = "JerniganLab/chat-data"
-DATA_FILENAME = "data.csv"
-DATA_FILE = os.path.join("data", DATA_FILENAME)
 HF_TOKEN = os.environ.get("HF_TOKEN")
 
-
-HF_TOKEN = os.environ.get("HF_TOKEN")
-
-# overriding/appending to the gradio template
-SCRIPT = """
-<script>
-if (!window.hasBeenRun) {
-    window.hasBeenRun = true;
-    console.log("should only happen once");
-    document.querySelector("button.submit").click();
-}
-</script>
-"""
-with open(os.path.join(gr.routes.STATIC_TEMPLATE_LIB, "frontend", "index.html"), "a") as f:
-    f.write(SCRIPT)
-
-try:
-    hf_hub_download(
-        repo_id=DATASET_REPO_ID,
-        filename=DATA_FILENAME,
-        cache_dir=DATA_DIRNAME,
-        repo_type='dataset',
-        force_filename=DATA_FILENAME
-    )
-except:
-    print("file not found")
-
-repo = Repository(
-    local_dir="data", clone_from=DATASET_REPO_URL, use_auth_token=HF_TOKEN
-)
-
-
-
-
 model_id = "JerniganLab/interviews-and-qa"
 base_model = "meta-llama/Meta-Llama-3-8B-Instruct"
 
@@ -64,17 +22,6 @@ pipeline = transformers.pipeline(
 
 pipeline.model = PeftModel.from_pretrained(llama_model, model_id)
 
-def store_message(message: str, system_prompt: str, response: str):
-    if response and message:
-        with open(DATA_FILE, "a") as csvfile:
-            writer = csv.DictWriter(csvfile, fieldnames=["message","system_prompt","response","time"])
-            writer.writerow(
-                {"message": message, "system_prompt": system_prompt, "response": response, "time": str(datetime.now())}
-            )
-        commit_url = repo.push_to_hub()
-    # return generate_html()
-
-
 # def chat_function(message, history, system_prompt, max_new_tokens, temperature):
 #     messages = [{"role":"system","content":system_prompt},
 #                 {"role":"user", "content":message}]
@@ -112,7 +59,6 @@ def chat_function(message, history, max_new_tokens, temperature):
         do_sample = True,
         temperature = temperature + 0.1,
         top_p = 0.9,)
-    store_message(message, system_prompt, outputs[0]["generated_text"][len(prompt):])
     return outputs[0]["generated_text"][len(prompt):]
 
 """
@@ -136,9 +82,7 @@ demo = gr.ChatInterface(
     additional_inputs=[
         gr.Slider(100,4000, label="Max New Tokens"),
         gr.Slider(0,1, label="Temperature")
-    ], 
-    type="messages",
-    save_history=True,
+    ]
     )