Update app.py

app.py

@@ -2,17 +2,25 @@ import streamlit as st
 from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
 import PyPDF2
 import torch
+import os
 
-st.set_page_config(page_title="Perplexity-style Q&A (OpenHermes)", layout="wide")
-st.title("🧠 Perplexity-style Study Assistant with OpenHermes-2.5")
+st.set_page_config(page_title="Perplexity-style Q&A (Mistral Auth)", layout="wide")
+st.title("🧠 AI Study Assistant using Mistral 7B (Authenticated)")
+
+# ✅ Load Hugging Face token from secrets
+hf_token = os.getenv("HF_TOKEN")
 
 @st.cache_resource
 def load_model():
-    tokenizer = AutoTokenizer.from_pretrained("teknium/OpenHermes-2.5-Mistral-7B")
+    tokenizer = AutoTokenizer.from_pretrained(
+        "mistralai/Mistral-7B-Instruct-v0.1",
+        token=hf_token
+    )
     model = AutoModelForCausalLM.from_pretrained(
-        "teknium/OpenHermes-2.5-Mistral-7B",
+        "mistralai/Mistral-7B-Instruct-v0.1",
         torch_dtype=torch.float16,
-        device_map="auto"
+        device_map="auto",
+        token=hf_token
     )
     pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512)
     return pipe
@@ -33,8 +41,7 @@ if uploaded_file:
 
 if st.button("Generate Answer"):
     with st.spinner("Generating answer..."):
-        prompt = f"<|system|>You are a helpful study assistant.<|user|>Context:\n{context}\n\nQuestion: {query}<|assistant|>"
+        prompt = f"[INST] Use the following context to answer the question:\n\n{context}\n\nQuestion: {query} [/INST]"
         result = textgen(prompt)[0]["generated_text"]
-        answer = result.replace(prompt, "").strip()
         st.success("Answer:")
-        st.write(answer)
+        st.write(result.replace(prompt, "").strip())