Update app.py

app.py

@@ -1,153 +1,111 @@
-import streamlit as st
-import pandas as pd
+import json
+import requests
 import openpyxl
-import docx
+import pandas as pd
+import streamlit as st
 from io import BytesIO
-from reportlab.pdfgen import canvas
 from reportlab.lib.pagesizes import letter
-from huggingface_hub import InferenceClient
-
-# 🔑 Get Hugging Face API Key from User
-API_KEY = st.text_input("Enter your Hugging Face API Key:", type="password")
-
-if API_KEY:
-    # Load Hugging Face TinyLlama Model
-    @st.cache_resource
-    def load_huggingface_client():
-        return InferenceClient(model="TinyLlama/TinyLlama-1.1B-Chat-v1.0", token=API_KEY)
-
-    client = load_huggingface_client()
-
-    # Function to generate AI text suggestions
-    def generate_suggestion(prompt):
-        response = client.text_generation(prompt, max_new_tokens=100)
-        return response.strip()
-
-    # Function to load Word document
-    def load_docx(file):
-        doc = docx.Document(file)
-        return "\n".join([para.text for para in doc.paragraphs])
-
-    # Function to save Word document
-    def save_docx(text):
-        output = BytesIO()
-        doc = docx.Document()
-        for line in text.split("\n"):
-            doc.add_paragraph(line)
-        doc.save(output)
-        return output
-
-    # Function to load Excel file
-    def load_excel(file):
-        return pd.ExcelFile(file)
-
-    # Function to edit Excel with full tools
-    def edit_excel(df, file_key):
-        st.write(f"### Editing Excel: {file_key}")
-
-        # Editable DataFrame
-        edited_df = st.data_editor(df, num_rows="dynamic", use_container_width=True)
-
-        # Apply Formatting Options
-        col_to_format = st.selectbox(f"Select Column to Format in {file_key}:", df.columns, key=f"col_{file_key}")
-        if st.button(f"Apply Bold Format - {file_key}", key=f"btn_{file_key}"):
-            edited_df[col_to_format] = edited_df[col_to_format].apply(lambda x: f"**{x}**")
-
-        return edited_df
-
-    # Function to generate PDF
-    def save_pdf(text):
-        output = BytesIO()
-        c = canvas.Canvas(output, pagesize=letter)
-        c.setFont("Helvetica", 12)
-        y_position = 750
-        for line in text.split("\n"):
-            c.drawString(100, y_position, line)
-            y_position -= 20
-        c.save()
-        return output
-
-    # 🏠 Streamlit UI
-    st.title("📂 RAG + TinyLlama NLP File Manager")
-    st.write("Upload, edit, and manage `.docx`, `.xls` files with AI-powered NLP and full Excel tools.")
-
-    # 📤 File Uploaders
-    uploaded_files = {}
-    for i in range(1, 5):
-        uploaded_files[f"File {i}"] = st.file_uploader(f"📄 Upload File {i}", type=["docx", "xls"])
-
-    # ✏️ File Editors
-    edited_docs = {}
-    edited_excels = {}
-
-    for file_key, file in uploaded_files.items():
-        if file:
-            st.subheader(f"📝 Editing {file_key}: {file.name}")
-
-            if file.name.endswith(".docx"):
-                text = load_docx(file)
-                edited_text = st.text_area(f"Edit {file.name}:", text, height=300)
-
-                # AI Text Suggestion
-                if st.button(f"✨ Generate AI Suggestion for {file.name}", key=f"suggest_{file_key}"):
-                    suggestion = generate_suggestion(edited_text)
-                    st.text_area("🤖 AI Suggestion:", suggestion, height=200)
-
-                edited_docs[file.name] = edited_text
-
-            elif file.name.endswith(".xls"):
-                xls = load_excel(file)
-                sheet_name = st.selectbox(f"📊 Select Sheet in {file.name}:", xls.sheet_names, key=f"sheet_{file_key}")
-                df = pd.read_excel(xls, sheet_name=sheet_name)
-                edited_df = edit_excel(df, file_key)
-                edited_excels[file.name] = edited_df
-
-    # 📥 Download Buttons
-    for file_name, text in edited_docs.items():
-        st.download_button(
-            label=f"📥 Download {file_name}",
-            data=save_docx(text).getvalue(),
-            file_name=file_name,
-            mime="application/vnd.openxmlformats-officedocument.wordprocessingml.document"
-        )
-
-        # PDF Download Option
-        st.download_button(
-            label=f"📥 Download {file_name} as PDF",
-            data=save_pdf(text).getvalue(),
-            file_name=f"{file_name}.pdf",
-            mime="application/pdf"
-        )
-
-    for file_name, df in edited_excels.items():
-        output = BytesIO()
-        with pd.ExcelWriter(output, engine="openpyxl") as writer:
-            df.to_excel(writer, index=False, sheet_name="EditedSheet")
-            writer.close()
-
-        st.download_button(
-            label=f"📥 Download {file_name}",
-            data=output.getvalue(),
-            file_name=file_name,
-            mime="application/octet-stream"  # FIXED MIME TYPE
-        )
-
-    # 🗨️ Chat with TinyLlama about Timetable Conflicts
-    st.subheader("🗓️ Timetable Conflict Checker")
-    query = st.text_input("Ask about conflicts in the uploaded Timetables:")
-
-    if query:
-        conflict_info = "No conflicts detected."  # Default response
-        for file_key, file in uploaded_files.items():
-            if file and file.name.endswith(".xls"):
-                xls = load_excel(file)
-                for sheet in xls.sheet_names:
-                    df = pd.read_excel(xls, sheet_name=sheet)
-                    if "Monday" in df.columns:  # Adjust this based on column headers
-                        monday_data = df["Monday"].dropna().astype(str).tolist()
-                        response = generate_suggestion(f"Analyze this schedule: {monday_data}. Find conflicts.")
-                        if "conflict" in response.lower():
-                            conflict_info = response
-
-        st.write("📢 **Conflict Analysis:**")
-        st.write(conflict_info)
+from reportlab.pdfgen import canvas
+
+# ✅ 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)
+
+# ✅ API Key Input
+st.sidebar.markdown("## 🔑 Enter Hugging Face API Key")
+hf_api_key = st.sidebar.text_input("API Key", type="password")
+
+# ✅ File Upload Section
+st.sidebar.markdown("## 📂 Upload Your Timetable Files")
+uploaded_file = st.sidebar.file_uploader("Upload Timetable", type=["xlsx"])
+
+# ✅ Load Timetable Data
+def load_timetable(file):
+    if file:
+        df = pd.read_excel(file)
+        return df
+    return None
+
+df = load_timetable(uploaded_file)
+
+# ✅ Ask TinyLlama API
+def ask_tinyllama_api(query):
+    if not hf_api_key:
+        return "Error: Please enter your API key."
+
+    url = "https://api-inference.huggingface.co/v1/chat/completions"
+    headers = {"Authorization": f"Bearer {hf_api_key}", "Content-Type": "application/json"}
+    payload = {
+        "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
+        "messages": [{"role": "user", "content": query}],
+        "max_tokens": 500
+    }
+
+    response = requests.post(url, headers=headers, json=payload)
+    if response.status_code == 200:
+        return response.json()["choices"][0]["message"]["content"]
+    else:
+        return f"API Error: {response.status_code} - {response.text}"
+
+# ✅ Auto-Schedule Missing Slots
+def auto_schedule(df):
+    if df is None:
+        return "No timetable uploaded."
+    
+    empty_slots = df[df.isnull().any(axis=1)].index.tolist()
+    for idx in empty_slots:
+        query = f"Suggest a subject and faculty for the empty slot in row {idx+2}."
+        suggestion = ask_tinyllama_api(query)
+        try:
+            subject, faculty = suggestion.split(", Faculty: ")
+            df.at[idx, "Subject"] = subject.strip()
+            df.at[idx, "Faculty"] = faculty.strip()
+        except:
+            continue
+    
+    return f"Auto-scheduling completed for {len(empty_slots)} slots."
+
+# ✅ Convert DataFrame to PDF
+def convert_to_pdf(df):
+    buffer = BytesIO()
+    pdf = canvas.Canvas(buffer, pagesize=letter)
+    pdf.setFont("Helvetica", 10)
+
+    pdf.drawString(30, 750, "AI-Powered Timetable")
+
+    y_position = 730
+    for i, row in df.iterrows():
+        pdf.drawString(30, y_position, str(row.values))
+        y_position -= 20
+        if y_position < 50:
+            pdf.showPage()
+            pdf.setFont("Helvetica", 10)
+            y_position = 750
+    
+    pdf.save()
+    buffer.seek(0)
+    return buffer
+
+# ✅ Display and Edit Timetable
+if df is not None:
+    st.markdown("## 📝 Edit Your Timetable")
+    edited_df = st.data_editor(df, key="editable_table")
+
+    # ✅ Auto-Schedule Button
+    if st.button("Auto-Schedule Missing Slots"):
+        result = auto_schedule(edited_df)
+        st.write("✅", result)
+
+    # ✅ Download as PDF
+    if st.button("Download as PDF"):
+        pdf_buffer = convert_to_pdf(edited_df)
+        st.download_button("📥 Download Timetable PDF", pdf_buffer, file_name="timetable.pdf", mime="application/pdf")
+
+# ✅ AI Query Section
+st.markdown("## 🤖 Ask TinyLlama 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_tinyllama_api(user_query)
+    st.write("🧠 **TinyLlama Suggests:**", ai_response)
+