Update app.py

app.py

@@ -1,21 +1,34 @@
+import os
 import gradio as gr
-from transformers import AutoTokenizer, AutoModelForCausalLM
+import requests
 
-# Load model directly
-tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-1B-Instruct")
-model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B-Instruct")
+# Run the Hugging Face CLI login command.
+os.system("huggingface-cli login")
 
+# Retrieve the API token from environment variables
+model_repo_id = os.getenv("MODEL_REPO_ID")
+if not model_repo_id:
+    raise ValueError("MODEL_REPO_ID token not found. Please set the MODEL_REPO_ID environment variable.")
+
+# Hugging Face Inference API Details
+API_URL = "https://api-inference.huggingface.co/models/meta-llama/Llama-3.2-1B-Instruct"
+HEADERS = {"Authorization": f"Bearer {model_repo_id}"}
 
 def generate_exegesis(passage):
     if not passage.strip():
         return "Please enter a Bible passage."
     
-    prompt = f"You are a professional Bible Scholar, Provide a detailed exegesis of the following biblical verse, including: The original Greek text and transliteration with Word-by-word analysis with meanings. Historical and cultural context. Theological significance. for:\n\n{passage}\n\nExegesis:"
+    prompt = (
+        "You are a professional Bible Scholar. Provide a detailed exegesis of the following biblical verse, including:"
+        " The original Greek text and transliteration with word-by-word analysis and meanings, historical and cultural context,"
+        " and theological significance for:\n\n"
+        f"{passage}\n\nExegesis:"
+    )
     
     payload = {
         "inputs": prompt,
         "parameters": {
-            "max_length": 250,
+            "max_length": 500,
             "temperature": 0.7,
             "do_sample": True
         }