Update app.py

app.py

@@ -1,37 +1,92 @@
 import streamlit as st
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import torch
+import requests
+import os
+from sentence_transformers import SentenceTransformer
+import faiss
+import numpy as np
 
-st.title("📚 AI Adaptive Learning")
+st.set_page_config(page_title="📚 AI Adaptive Learning (Ultimate)", layout="wide")
+st.title("📚 AI Adaptive Learning & Smart Revision System (Mistral 7B)")
 
-MODEL_ID = "microsoft/phi-2"
+# -----------------------------
+# 1️⃣ Hugging Face API Setup
+# -----------------------------
+HF_API_TOKEN = st.secrets["HF_API_TOKEN"]
+MODEL_ID = "mistralai/Mistral-7B-Instruct-v0.2"
+API_URL = f"https://api-inference.huggingface.co/models/{MODEL_ID}"
 
+HEADERS = {"Authorization": f"Bearer {HF_API_TOKEN}"}
+
+def query_hf_api(prompt):
+    payload = {
+        "inputs": prompt,
+        "parameters": {"max_new_tokens": 250, "temperature": 0.7, "top_p":0.9}
+    }
+    response = requests.post(API_URL, headers=HEADERS, json=payload)
+    if response.status_code == 200:
+        output = response.json()
+        return output[0]["generated_text"]
+    else:
+        return f"Error: {response.status_code} - {response.text}"
+
+# -----------------------------
+# 2️⃣ Build RAG (Context Retrieval)
+# -----------------------------
 @st.cache_resource
-def load_model():
-    tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
-    model = AutoModelForCausalLM.from_pretrained(
-        MODEL_ID,
-        torch_dtype=torch.float32,
-        device_map="auto"
-    )
-    return tokenizer, model
-
-tokenizer, model = load_model()
-
-user_input = st.text_input("Ask a question:")
-
-if st.button("Submit") and user_input:
-    inputs = tokenizer(user_input, return_tensors="pt")
-
-    with torch.no_grad():
-        outputs = model.generate(
-            **inputs,
-            max_new_tokens=150,
-            do_sample=True,
-            temperature=0.7
-        )
-
-    answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
-
-    st.subheader("AI Answer:")
-    st.write(answer)
+def build_rag_index(doc_folder="docs"):
+    embeddings = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
+    corpus = []
+    corpus_texts = []
+
+    for file_name in os.listdir(doc_folder):
+        if file_name.endswith(".txt"):
+            path = os.path.join(doc_folder, file_name)
+            with open(path, "r", encoding="utf-8") as f:
+                text = f.read()
+                sentences = text.split("\n")
+                for sent in sentences:
+                    if sent.strip():
+                        corpus.append(embeddings.encode(sent))
+                        corpus_texts.append(sent)
+
+    dim = corpus[0].shape[0]
+    index = faiss.IndexFlatL2(dim)
+    index.add(np.array(corpus).astype("float32"))
+    return index, corpus_texts, embeddings
+
+index, corpus_texts, embedder = build_rag_index()
+
+# -----------------------------
+# 3️⃣ Retrieve Context
+# -----------------------------
+def retrieve_context(query, k=3):
+    query_vec = embedder.encode(query).astype("float32")
+    D, I = index.search(np.array([query_vec]), k)
+    context = "\n".join([corpus_texts[i] for i in I[0]])
+    return context
+
+# -----------------------------
+# 4️⃣ Streamlit UI
+# -----------------------------
+difficulty = st.selectbox("Select explanation style:", ["Explain Like I'm 10", "Detailed", "Step-by-Step"])
+
+user_question = st.text_input("Ask a question:")
+
+if st.button("Submit") and user_question:
+    with st.spinner("Generating high-quality answer..."):
+        context = retrieve_context(user_question, k=3)
+        prompt = f"""
+You are a knowledgeable teacher and explain concepts clearly.
+
+Context:
+{context}
+
+Question:
+{user_question}
+
+Explain the answer in a {difficulty} manner.
+Answer:
+"""
+        answer = query_hf_api(prompt)
+        st.subheader("📖 AI Answer:")
+        st.write(answer)