Create app.py

app.py

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+import json
+import os
+import requests
+import streamlit as st
+import pandas as pd
+import openpyxl
+import torch
+from reportlab.lib.pagesizes import letter
+from reportlab.pdfgen import canvas
+from huggingface_hub import InferenceClient
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+
+# ✅ Secure API Key Input (User provides it manually)
+HF_API_KEY = st.sidebar.text_input("Enter your Hugging Face API Key", type="password")
+
+# ✅ Initialize Hugging Face API Client
+client = InferenceClient(token=HF_API_KEY) if HF_API_KEY else None
+
+# ✅ Load Local Model with Device Optimization
+MODEL_NAME = "mistralai/Mistral-7B-Instruct-v0.2"
+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)
+
+# ✅ 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"])
+
+uploaded_files = {
+    "Master Timetable": uploaded_master,
+    "Lab Timetable": uploaded_lab,
+    "Classroom Timetable": uploaded_classroom,
+    "Individual Timetable": uploaded_individual,
+}
+
+# ✅ Load Timetable Data (Directly from Uploaded File)
+def load_timetable(file):
+    if not file:
+        return None
+    df = pd.read_excel(file)
+    return df
+
+# ✅ Ask Mistral AI via API with Timetable Context
+def ask_mistral_api_with_timetable(query, timetable_df):
+    if timetable_df is None:
+        return "No timetable uploaded."
+
+    # Convert timetable to text format
+    timetable_text = timetable_df.to_string(index=False)
+
+    prompt = f"Here is a timetable:\n\n{timetable_text}\n\nUser Query: {query}\n\nAnswer based on the timetable."
+
+    headers = {"Authorization": f"Bearer {HF_API_KEY}"}
+    payload = {
+        "model": MODEL_NAME,
+        "messages": [{"role": "user", "content": prompt}],
+        "max_tokens": 500,
+    }
+
+    response = requests.post("https://api-inference.huggingface.co/v1/chat/completions", json=payload, headers=headers)
+
+    if response.status_code == 200:
+        return response.json()["choices"][0]["message"]["content"]
+    else:
+        return f"Error: {response.json()}"
+
+# ✅ AI Query Section (with Timetable Scanning)
+st.markdown("## 🤖 Ask Mistral AI About Your Timetable")
+selected_file = st.selectbox("Choose a timetable file to scan:", list(uploaded_files.keys()))
+
+if uploaded_files[selected_file]:
+    timetable_df = load_timetable(uploaded_files[selected_file])
+    user_query = st.text_input("Type your question (e.g., 'Who is free at 10 AM on Monday?')")
+
+    if st.button("Ask AI via API with Timetable"):
+        ai_response = ask_mistral_api_with_timetable(user_query, timetable_df)
+        st.write("🧠 **Mistral AI Suggests:**", ai_response)
+else:
+    st.write("⚠️ Please upload a timetable file first.")
+
+# ✅ Display Uploaded Timetables
+st.markdown("## 📜 View Uploaded Timetables")
+
+for name, file in uploaded_files.items():
+    if file:
+        df = pd.read_excel(file)
+        st.markdown(f"### {name}")
+        st.dataframe(df)
+