Update app.py

app.py

@@ -1,48 +1,72 @@
 import streamlit as st
 import requests
 
-HF_API_TOKEN = st.secrets["HF_API_TOKEN"]
+# -----------------------------
+# Hugging Face API setup
+# -----------------------------
+HF_API_TOKEN = st.secrets["HF_API_TOKEN"]  # Add token in Space Secrets
 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}"}
 
+# -----------------------------
+# Helper function to query model
+# -----------------------------
 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"]
+    payload = {
+        "inputs": prompt,
+        "parameters": {"max_new_tokens": max_length, "temperature": 0.7},
+    }
+    try:
+        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 str(result)
-    else:
-        return f"Request failed: {response.status_code}"
+            return f"Request failed: {response.status_code}"
+    except Exception as e:
+        return f"Error: {e}"
 
+# -----------------------------
+# Streamlit UI
+# -----------------------------
 st.set_page_config(page_title="AI Adaptive Learning", layout="wide")
 st.title("📚 AI Adaptive Learning App")
+st.write("Ask a question and get AI-generated explanations. Track your understanding with a score.")
 
-# Session state
+# Session state for adaptive learning
 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 = ""
 
+# User input
 user_input = st.text_input("Your Question:")
+
 if st.button("Submit") and 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 = answer
 
+# Display AI answer
 if st.session_state.last_answer:
     st.subheader("AI Answer:")
     st.write(st.session_state.last_answer)
 
+# Adaptive learning score
 st.subheader("Your Adaptive Learning Score")
 col1, col2 = st.columns(2)
 with col1:
-    if st.button("I understood this"): st.session_state.score += 1
+    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
+    if st.button("I didn't understand"):
+        st.session_state.score -= 1
+
 st.metric(label="Learning Score", value=st.session_state.score)