Update app.py

app.py

@@ -1,69 +1,48 @@
 import streamlit as st
-from transformers import AutoModelForCausalLM, AutoTokenizer
-import torch
+import requests
+
+HF_API_TOKEN = st.secrets["HF_API_TOKEN"]
+MODEL_ID = "meta-llama/Llama-2-7b-chat-hf"
+API_URL = f"https://api-inference.huggingface.co/models/{MODEL_ID}"
+HEADERS = {"Authorization": f"Bearer {HF_API_TOKEN}"}
+
+def query_hf_model(prompt, max_length=256):
+    payload = {"inputs": prompt, "parameters": {"max_new_tokens": max_length, "temperature":0.7}}
+    response = requests.post(API_URL, headers=HEADERS, json=payload, timeout=60)
+    if response.status_code == 200:
+        result = response.json()
+        if isinstance(result, list) and "generated_text" in result[0]:
+            return result[0]["generated_text"]
+        elif "error" in result:
+            return "Error from model: " + result["error"]
+        else:
+            return str(result)
+    else:
+        return f"Request failed: {response.status_code}"
+
+st.set_page_config(page_title="AI Adaptive Learning", layout="wide")
+st.title("📚 AI Adaptive Learning App")
+
+# Session state
+if "score" not in st.session_state: st.session_state.score = 0
+if "last_question" not in st.session_state: st.session_state.last_question = ""
+if "last_answer" not in st.session_state: st.session_state.last_answer = ""
 
-# -----------------------------
-# Initialize model
-# -----------------------------
-@st.cache_resource(show_spinner=True)
-def load_model(model_name):
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-    model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")
-    return tokenizer, model
-
-# You can choose any LLaMA variant on Hugging Face
-MODEL_NAME = "meta-llama/Llama-2-7b-chat-hf"
-
-tokenizer, model = load_model(MODEL_NAME)
-
-# -----------------------------
-# Helper function for inference
-# -----------------------------
-def generate_answer(prompt, max_length=256, temperature=0.7):
-    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
-    output = model.generate(
-        **inputs,
-        max_new_tokens=max_length,
-        do_sample=True,
-        temperature=temperature,
-        top_p=0.95,
-        eos_token_id=tokenizer.eos_token_id,
-    )
-    answer = tokenizer.decode(output[0], skip_special_tokens=True)
-    return answer
-
-# -----------------------------
-# Streamlit UI
-# -----------------------------
-st.set_page_config(page_title="Adaptive AI Learning", layout="wide")
-st.title("📚 Adaptive AI Learning App")
-
-if "score" not in st.session_state:
-    st.session_state.score = 0
-
-st.write("Ask any question to learn!")
-
-# User input
 user_input = st.text_input("Your Question:")
-
 if st.button("Submit") and user_input:
-    with st.spinner("AI is thinking..."):
-        response = generate_answer(user_input)
+    with st.spinner("Generating AI answer..."):
+        answer = query_hf_model(user_input)
     st.session_state.last_question = user_input
-    st.session_state.last_answer = response
-    st.success("Answer Generated!")
+    st.session_state.last_answer = answer
 
-if "last_answer" in st.session_state:
+if st.session_state.last_answer:
     st.subheader("AI Answer:")
     st.write(st.session_state.last_answer)
 
-# -----------------------------
-# Adaptive Learning (simple logic)
-# -----------------------------
-st.subheader("Your Adaptive Score")
-if st.button("I understood this"):
-    st.session_state.score += 1
-if st.button("I didn't understand"):
-    st.session_state.score -= 1
-
+st.subheader("Your Adaptive Learning Score")
+col1, col2 = st.columns(2)
+with col1:
+    if st.button("I understood this"): st.session_state.score += 1
+with col2:
+    if st.button("I didn't understand"): st.session_state.score -= 1
 st.metric(label="Learning Score", value=st.session_state.score)