Update app.py

app.py

@@ -7,129 +7,129 @@ from reportlab.pdfgen import canvas
 from reportlab.lib.pagesizes import letter
 from huggingface_hub import InferenceClient
 
-# Hugging Face API Key
-API_KEY = "hf_xxxxxxxxxxxxxxxxxxxxxxxx"  # Replace with your Hugging Face 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 using TinyLlama
-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 file with full tool support
-def edit_excel(df):
-    st.write("### Excel Editor with Full Editing Tools")
-    
-    # Display DataFrame and allow modifications
-    edited_df = st.data_editor(df, num_rows="dynamic", use_container_width=True)
-
-    # Provide additional Excel formatting options
-    col_to_format = st.selectbox("Select Column to Format:", df.columns)
-    if st.button("Apply Bold Format"):
-        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("Advanced RAG + TinyLlama NLP File Manager")
-st.write("Upload, edit, and manage `.docx`, `.xls` files, with full Excel tools and AI-powered NLP.")
-
-# File Uploaders
-uploaded_files = [None] * 4
-file_types = ["docx", "xls", "docx", "xls"]
-
-for i in range(4):
-    uploaded_files[i] = st.file_uploader(f"Upload File {i+1} ({file_types[i]})", type=[file_types[i]])
-
-# File Editors
-edited_docs = {}
-edited_excels = {}
-
-for i, file in enumerate(uploaded_files):
-    if file:
-        st.subheader(f"Editing File {i+1}: {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}"):
-                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)
-            df = pd.read_excel(xls, sheet_name=sheet_name)
-            edited_df = edit_excel(df)
-            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/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
-    )
-    
+# 🔑 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):
+        st.write("### Excel Editor with Full Editing Tools")
+        
+        # Editable DataFrame
+        edited_df = st.data_editor(df, num_rows="dynamic", use_container_width=True)
+
+        # Apply Formatting Options
+        col_to_format = st.selectbox("Select Column to Format:", df.columns)
+        if st.button("Apply Bold Format"):
+            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 = [None] * 4
+    file_types = ["docx", "xls", "docx", "xls"]
+
+    for i in range(4):
+        uploaded_files[i] = st.file_uploader(f"📄 Upload File {i+1} ({file_types[i]})", type=[file_types[i]])
+
+    # ✏️ File Editors
+    edited_docs = {}
+    edited_excels = {}
+
+    for i, file in enumerate(uploaded_files):
+        if file:
+            st.subheader(f"📝 Editing File {i+1}: {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}"):
+                    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)
+                df = pd.read_excel(xls, sheet_name=sheet_name)
+                edited_df = edit_excel(df)
+                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/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
+        )