Create app.py

app.py

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+import json
+import os
+import streamlit as st
+import pandas as pd
+import openpyxl
+import io
+import torch
+from reportlab.lib.pagesizes import letter
+from reportlab.pdfgen import canvas
+from huggingface_hub import InferenceClient
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+
+# Load API key securely
+with open("secrets.json", "r") as file:
+    secrets = json.load(file)
+HF_API_KEY = secrets["HF_API_KEY"]
+
+client = InferenceClient(api_token=HF_API_KEY)
+
+# Load Local Model with Device Optimization
+MODEL_NAME = "mistralai/Mistral-7B-Instruct-v0.3"
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForCausalLM.from_pretrained(MODEL_NAME).to(device)
+pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, device=0 if device == "cuda" else -1)
+
+# Initialize session state for uploaded files
+if "uploaded_files" not in st.session_state:
+    st.session_state.uploaded_files = {}
+
+# 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)
+
+# 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"])
+
+# Save uploaded files locally
+if uploaded_master:
+    st.session_state.uploaded_files["Master Timetable"] = uploaded_master
+if uploaded_lab:
+    st.session_state.uploaded_files["Lab Timetable"] = uploaded_lab
+if uploaded_classroom:
+    st.session_state.uploaded_files["Classroom Timetable"] = uploaded_classroom
+if uploaded_individual:
+    st.session_state.uploaded_files["Individual Timetable"] = uploaded_individual
+
+# Define paths for uploaded files
+TIMETABLE_FILES = {name: file for name, file in st.session_state.uploaded_files.items()}
+
+# Load Timetable Data
+def load_timetable(sheet_name):
+    if sheet_name not in TIMETABLE_FILES:
+        return None
+    file = TIMETABLE_FILES[sheet_name]
+    wb = openpyxl.load_workbook(file)
+    sheet = wb.active
+    return [row for row in sheet.iter_rows(values_only=True)]
+
+# Ask Mistral AI a question using API
+def ask_mistral_api(query):
+    response = client.text_generation(model=MODEL_NAME, inputs=query, max_new_tokens=500)
+    return response
+
+# Ask Mistral AI a question using local model
+def ask_mistral_local(query):
+    inputs = tokenizer(query, return_tensors="pt").to(device)
+    outputs = model.generate(**inputs, max_new_tokens=200)
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response
+
+# Auto-Schedule Missing Slots
+def auto_schedule(sheet_name):
+    if sheet_name not in TIMETABLE_FILES:
+        return "No timetable uploaded."
+
+    file = TIMETABLE_FILES[sheet_name]
+    local_path = f"temp_{sheet_name.replace(' ', '_')}.xlsx"
+    
+    with open(local_path, "wb") as f:
+        f.write(file.getbuffer())
+
+    wb = openpyxl.load_workbook(local_path)
+    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_mistral_local(query)
+
+        try:
+            subject, faculty = suggestion.split(", Faculty: ")
+            subject = subject.replace("Subject: ", "").strip()
+            faculty = faculty.strip()
+            sheet.cell(row=row_idx, column=4, value=subject)
+            sheet.cell(row=row_idx, column=5, value=faculty)
+        except:
+            continue  
+
+    wb.save(local_path)
+    return f"Auto-scheduling completed for {len(empty_slots)} slots."
+
+# AI Query Section
+st.markdown("## 🤖 Ask Mistral 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"):
+    ai_response = ask_mistral_api(user_query)
+    st.write("🧠 **Mistral AI Suggests:**", ai_response)
+
+if st.button("Ask AI via Local Model"):
+    ai_response = ask_mistral_local(user_query)
+    st.write("🧠 **Mistral AI Suggests:**", ai_response)
+
+# 🚀 Now Your App is Fully Functional & Optimized! 🚀