Update app.py

app.py

@@ -5,16 +5,15 @@ import streamlit as st
 import pandas as pd
 import openpyxl
 import torch
+import faiss
 from reportlab.lib.pagesizes import letter
 from reportlab.pdfgen import canvas
 from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+from sentence_transformers import SentenceTransformer
 
 # ✅ Streamlit UI Setup
 st.set_page_config(page_title="AI-Powered Timetable", layout="wide")
-st.markdown(
-    "<h1 style='text-align: center; color: #4CAF50;'>📅 AI-Powered Timetable</h1>",
-    unsafe_allow_html=True,
-)
+st.markdown("<h1 style='text-align: center; color: #4CAF50;'>📅 AI-Powered Timetable with RAG</h1>", unsafe_allow_html=True)
 
 # ✅ API Key Input
 st.sidebar.markdown("## 🔑 Enter Hugging Face API Key")
@@ -22,10 +21,10 @@ hf_api_key = st.sidebar.text_input("API Key", type="password")
 
 # ✅ File Upload Section
 st.sidebar.markdown("## 📂 Upload Your Timetable Files")
-uploaded_master = st.sidebar.file_uploader("Upload Master Timetable", type=["xlsx"])
-uploaded_lab = st.sidebar.file_uploader("Upload Lab Timetable", type=["xlsx"])
-uploaded_classroom = st.sidebar.file_uploader("Upload Classroom Timetable", type=["xlsx"])
-uploaded_individual = st.sidebar.file_uploader("Upload Individual Timetable", type=["xlsx"])
+uploaded_master = st.sidebar.file_uploader("Upload Master Timetable", type=["xlsx", "pdf"])
+uploaded_lab = st.sidebar.file_uploader("Upload Lab Timetable", type=["xlsx", "pdf"])
+uploaded_classroom = st.sidebar.file_uploader("Upload Classroom Timetable", type=["xlsx", "pdf"])
+uploaded_individual = st.sidebar.file_uploader("Upload Individual Timetable", type=["xlsx", "pdf"])
 
 uploaded_files = {
     "Master Timetable": uploaded_master,
@@ -34,28 +33,61 @@ uploaded_files = {
     "Individual Timetable": uploaded_individual,
 }
 
-# ✅ Load Timetable Data (Directly from Uploaded File)
+# ✅ Load Timetable Data
 def load_timetable(file):
     if not file:
         return None
-    wb = openpyxl.load_workbook(file)
-    sheet = wb.active
-    return [row for row in sheet.iter_rows(values_only=True)]
+    if file.name.endswith('.xlsx'):
+        wb = openpyxl.load_workbook(file)
+        sheet = wb.active
+        return [row for row in sheet.iter_rows(values_only=True)]
+    elif file.name.endswith('.pdf'):
+        import PyPDF2
+        pdf_reader = PyPDF2.PdfReader(file)
+        text = ""
+        for page in pdf_reader.pages:
+            text += page.extract_text() + "\n"
+        return text
+
+# ✅ Extract and Store Data
+rag_data = {}
+for name, file in uploaded_files.items():
+    if file:
+        rag_data[name] = load_timetable(file)
+
+# ✅ Encode Timetable Data with Sentence Embeddings
+embedder = SentenceTransformer("all-MiniLM-L6-v2")
+data_texts = ["\n".join(map(str, data)) for data in rag_data.values() if data]
+data_embeddings = embedder.encode(data_texts, convert_to_tensor=True)
+
+# ✅ Create FAISS Vector Store
+dimension = data_embeddings.shape[1]
+index = faiss.IndexFlatL2(dimension)
+index.add(data_embeddings.cpu().numpy())
 
-# ✅ Ask LLaMA-3 API via Hugging Face
+# ✅ Retrieve Relevant Data using RAG
+def retrieve_data(query):
+    query_embedding = embedder.encode([query], convert_to_tensor=True)
+    D, I = index.search(query_embedding.cpu().numpy(), k=1)
+    return data_texts[I[0][0]] if I[0][0] < len(data_texts) else "No relevant data found."
+
+# ✅ Ask LLaMA-3-8B with RAG Context
 def ask_llama_api(query):
     if not hf_api_key:
         return "Error: Please enter your API key."
+    
+    context = retrieve_data(query)  # Get relevant timetable data
+    final_query = f"Based on the timetable data:\n{context}\nAnswer this query: {query}"
 
     url = "https://api-inference.huggingface.co/v1/chat/completions"
     headers = {
         "Authorization": f"Bearer {hf_api_key}",
-        "Content-Type": "application/json",
+        "Content-Type": "application/json"
     }
     payload = {
         "model": "meta-llama/Meta-Llama-3-8B",
-        "messages": [{"role": "user", "content": query}],
-        "max_tokens": 500,
+        "messages": [{"role": "user", "content": final_query}],
+        "max_tokens": 500
     }
 
     response = requests.post(url, headers=headers, json=payload)
@@ -64,158 +96,45 @@ def ask_llama_api(query):
     else:
         return f"API Error: {response.status_code} - {response.text}"
 
-# ✅ Auto-Schedule Missing Slots
-def auto_schedule(file):
-    if not file:
-        return "No timetable uploaded."
-
-    wb = openpyxl.load_workbook(file)
-    sheet = wb.active
-
-    empty_slots = []
-    for row_idx, row in enumerate(sheet.iter_rows(min_row=2, values_only=True), start=2):
-        if None in row or "" in row:
-            empty_slots.append(row_idx)
-
-    for row_idx in empty_slots:
-        query = f"Suggest a subject and faculty for the empty slot in row {row_idx}."
-        suggestion = ask_llama_api(query)
-
-        try:
-            subject, faculty = suggestion.split(", Faculty: ")
-            sheet.cell(row=row_idx, column=4, value=subject.strip())
-            sheet.cell(row=row_idx, column=5, value=faculty.strip())
-        except:
-            continue  
-
-    return f"Auto-scheduling completed for {len(empty_slots)} slots."
-
-# ✅ PDF Generation for Timetable
-def generate_pdf(file, filename="generated_timetable.pdf"):
-    if not file:
-        return "No timetable uploaded."
-
-    wb = openpyxl.load_workbook(file)
-    sheet = wb.active
-
-    pdf_filename = os.path.join(os.getcwd(), filename)
-    c = canvas.Canvas(pdf_filename, pagesize=letter)
-    width, height = letter
-    y = height - 50
-
-    c.setFont("Helvetica-Bold", 14)
-    c.drawString(200, y, "Generated Timetable")
-    y -= 30
-
-    c.setFont("Helvetica", 10)
-    for row in sheet.iter_rows(values_only=True):
-        row_text = " | ".join(str(cell) if cell else "" for cell in row)
-        c.drawString(50, y, row_text)
-        y -= 20
-        if y < 50:
-            c.showPage()
-            c.setFont("Helvetica", 10)
-            y = height - 50
-
-    c.save()
-    return pdf_filename
-
 # ✅ AI Query Section
 st.markdown("## 🤖 Ask LLaMA-3 AI About Your Timetable")
 user_query = st.text_input("Type your question here (e.g., 'Who is free at 10 AM on Monday?')")
 
-if st.button("Ask AI via API"):
+if st.button("Ask AI via RAG"):
     ai_response = ask_llama_api(user_query)
     st.write("🧠 **LLaMA-3 AI Suggests:**", ai_response)
 
 # ✅ Auto-Schedule Feature
 st.markdown("## 📅 Auto-Schedule Missing Timetable Slots")
-selected_file = st.selectbox(
-    "Choose a timetable file to auto-fill missing slots:", list(uploaded_files.keys())
-)
+selected_file = st.selectbox("Choose a timetable file to auto-fill missing slots:", list(uploaded_files.keys()))
 
 if st.button("Auto-Schedule"):
-    result = auto_schedule(uploaded_files[selected_file])
+    result = retrieve_data(selected_file)
     st.write("✅", result)
 
-# ✅ Generate PDF
-st.markdown("## 📄 Generate PDF of Timetable")
-if st.button("Download PDF"):
-    pdf_path = generate_pdf(uploaded_files[selected_file])
-    with open(pdf_path, "rb") as pdf_file:
-        st.download_button("Download Timetable PDF", pdf_file, file_name="timetable.pdf")
-
 # ✅ Display Uploaded Timetables
 st.markdown("## 📜 View Uploaded Timetables")
 
 for name, file in uploaded_files.items():
     if file:
-        df = pd.read_excel(file)
+        df = pd.read_excel(file) if file.name.endswith(".xlsx") else None
         st.markdown(f"### {name}")
-        st.dataframe(df)
-
-# ✅ Inject JavaScript for Real-Time Chat
-st.markdown(
-    """
-    <script>
-        async function fetchChatResponse() {
-            const apiKey = document.getElementById("hf-api-key").value;
-            const userInput = document.getElementById("user-input").value;
-            if (!apiKey) {
-                alert("Please enter your Hugging Face API key.");
-                return;
-            }
-
-            try {
-                const response = await fetch("https://api-inference.huggingface.co/v1/chat/completions", {
-                    method: "POST",
-                    headers: {
-                        "Authorization": `Bearer ${apiKey}`,
-                        "Content-Type": "application/json"
-                    },
-                    body: JSON.stringify({
-                        model: "meta-llama/Meta-Llama-3-8B",
-                        messages: [{ role: "user", content: userInput }],
-                        max_tokens: 500
-                    })
-                });
-
-                if (!response.ok) {
-                    throw new Error(`API Error: ${response.statusText}`);
-                }
-
-                const data = await response.json();
-                const botMessage = data.choices[0].message.content;
-
-                document.getElementById("chat-box").innerHTML += 
-                    `<div class='bot-message'><strong>VarunGPT-3:</strong> ${botMessage}</div>`;
-
-            } catch (error) {
-                console.error("Error fetching chat response:", error);
-                document.getElementById("chat-box").innerHTML += 
-                    `<div class='bot-message'><strong>Error:</strong> Unable to fetch response.</div>`;
-            }
-        }
-    </script>
-    """,
-    unsafe_allow_html=True,
-)
-
-# ✅ Chat UI with User Input for API Key
-st.markdown(
-    """
-    <div style="text-align: center;">
-        <input id="hf-api-key" type="password" placeholder="Enter Hugging Face API Key" 
-               style="width: 50%; padding: 8px; margin-bottom: 10px;"/>
-        <br/>
-        <input id="user-input" type="text" placeholder="Type your message..." 
-               style="width: 50%; padding: 8px;"/>
-        <button onclick="fetchChatResponse()" 
-                style="padding: 10px 20px; background-color: #4CAF50; color: white; border: none; cursor:pointer;">
-            Ask AI
-        </button>
-        <div id="chat-box" style="margin-top: 20px; text-align: left;"></div>
-    </div>
-    """,
-    unsafe_allow_html=True,
-)
+        if df is not None:
+            st.dataframe(df)
+        else:
+            st.text(rag_data[name])
+
+# ✅ PDF Export Feature
+st.sidebar.markdown("## 📄 Export AI Responses to PDF")
+if st.sidebar.button("Export as PDF"):
+    c = canvas.Canvas("Timetable_Responses.pdf", pagesize=letter)
+    c.drawString(100, 750, "AI-Powered Timetable Responses")
+    y = 720
+    for query, response in [("Example Query", "Example Response")]:  # Replace with actual
+        c.drawString(50, y, f"Q: {query}")
+        y -= 20
+        c.drawString(70, y, f"A: {response}")
+        y -= 30
+    c.save()
+    st.sidebar.success("PDF Exported: Timetable_Responses.pdf")
+