Altered token

app.py

@@ -1,5 +1,4 @@
-import random
-import time
+import os
 import torch
 import gradio as gr
 import requests
@@ -9,7 +8,8 @@ from textwrap import wrap, fill
 
 ## using Falcon 7b Instruct
 Falcon_API_URL = "https://api-inference.huggingface.co/models/tiiuae/falcon-7b-instruct"
-HEADERS = {"Authorization": "Bearer <HF TOKEN>"}
+hf_token = os.getenv("HUGGINGFACE_TOKEN")
+HEADERS = {"Authorization": "Bearer {hf_token}"}
 def falcon_query(payload):
     response = requests.post(Falcon_API_URL, headers=HEADERS, json=payload)
     return response.json()
@@ -33,40 +33,6 @@ def wrap_text(text, width=90):
     wrapped_text = '\n'.join(wrapped_lines)
     return wrapped_text
 
-def multimodal_prompt(user_input, system_prompt):
-    """
-    Generates text using a large language model, given a user input and a system prompt.
-    Args:
-        user_input: The user's input text to generate a response for.
-        system_prompt: Optional system prompt.
-    Returns:
-        A string containing the generated text in the Falcon-like format.
-    """
-    # Combine user input and system prompt
-    formatted_input = f"{{{{ {system_prompt} }}}}\nUser: {user_input}\nFalcon:"
-
-    # Encode the input text
-    encodeds = tokenizer(formatted_input, return_tensors="pt", add_special_tokens=False)
-    model_inputs = encodeds.to(device)
-
-    # Generate a response using the model
-    output = peft_model.generate(
-        **model_inputs,
-        max_length=500,
-        use_cache=True,
-        early_stopping=False,
-        bos_token_id=peft_model.config.bos_token_id,
-        eos_token_id=peft_model.config.eos_token_id,
-        pad_token_id=peft_model.config.eos_token_id,
-        temperature=0.4,
-        do_sample=True
-    )
-
-    # Decode the response
-    response_text = tokenizer.decode(output[0], skip_special_tokens=True)
-
-    return response_text
-
 class ChatbotInterface():
     def __init__(self, name, system_prompt="You are an expert medical analyst that helps users with any medical related information."):
         self.name = name