app.py2025_brick

import os
import streamlit as st
import tempfile
import io
import pandas as pd
import zipfile
import PyPDF2

# Importe für das Gemini SDK
import google.generativeai as genai
from google.generativeai.errors import APIError
from PIL import Image # Bleibt, um PIL-Objekte zu behandeln

# ----------------------------------------------------
# 🚨 BEHOBENE KRITISCHE FIXES (Du hast diese bereits!)
# Wird beibehalten, um die Stabilität in restriktiven Umgebungen zu gewährleisten.
# ----------------------------------------------------
TEMP_STREAMLIT_HOME = os.path.join(tempfile.gettempdir(), "st_config_workaround")
os.makedirs(TEMP_STREAMLIT_HOME, exist_ok=True)
os.environ["STREAMLIT_HOME"] = TEMP_STREAMLIT_HOME
os.environ["STREAMLIT_GATHER_USAGE_STATS"] = "false"
CONFIG_PATH = os.path.join(TEMP_STREAMLIT_HOME, "config.toml")
CONFIG_CONTENT = """
[browser]
gatherUsageStats = false
"""
if not os.path.exists(CONFIG_PATH):
    try:
        with open(CONFIG_PATH, "w") as f:
            f.write(CONFIG_CONTENT)
    except:
        pass # Ignoriere, wenn das Schreiben in /tmp fehlschlägt

# ----------------------------------------------------
# ENDE DER WORKAROUNDS
# ----------------------------------------------------


# --- Konfiguration der Seite ---
st.set_page_config(page_title="Gemini AI Chat", layout="wide", initial_sidebar_state="expanded")
st.title("🤖 Gemini AI Chat Interface")
st.markdown("""
**Welcome to the Gemini AI Chat Interface!**
Chat seamlessly with Google's advanced Gemini AI models, supporting multiple input types.
""")

# Session State Management
if "messages" not in st.session_state:
    st.session_session.messages = []
if "uploaded_content" not in st.session_state:
    st.session_state.uploaded_content = None

# --- Funktionen zur Dateiverarbeitung ---

# 🛑 encode_image wird entfernt, da das SDK PIL-Objekte direkt verarbeitet.

def process_file(uploaded_file):
    """Verarbeitet die hochgeladene Datei und extrahiert den Inhalt."""
    file_type = uploaded_file.name.split('.')[-1].lower()
    text_extensions = ('.txt', '.csv', '.py', '.html', '.js', '.css', '.json', '.xml', '.sql', '.xlsx')
    
    if file_type in ["jpg", "jpeg", "png"]:
        # WICHTIG: Das PIL-Image-Objekt direkt speichern
        return {"type": "image", "content": Image.open(uploaded_file).convert('RGB')} 
    
    if file_type in ["txt"] + [ext.strip('.') for ext in text_extensions if ext not in ('.csv', '.xlsx')]:
        return {"type": "text", "content": uploaded_file.read().decode("utf-8", errors='ignore')}
    
    if file_type in ["csv", "xlsx"]:
        try:
            df = pd.read_csv(uploaded_file) if file_type == "csv" else pd.read_excel(uploaded_file)
            return {"type": "text", "content": df.to_string()}
        except Exception as e:
            return {"type": "error", "content": f"Failed to read tabular data: {e}"}
    
    if file_type == "pdf":
        try:
            reader = PyPDF2.PdfReader(uploaded_file)
            return {"type": "text", "content": "".join(page.extract_text() for page in reader.pages if page.extract_text())}
        except Exception as e:
            return {"type": "error", "content": f"Failed to read PDF: {e}"}
    
    if file_type == "zip":
        try:
            with zipfile.ZipFile(uploaded_file) as z:
                newline = "\n"
                content = f"ZIP Contents (Processing text files only):{newline}"
                for file_info in z.infolist():
                    if not file_info.is_dir() and file_info.filename.lower().endswith(text_extensions):
                        with z.open(file_info.filename) as file:
                            file_content = file.read().decode('utf-8', errors='ignore')
                            content += f"{newline}📄 {file_info.filename}:{newline}{file_content}{newline}"
                    elif not file_info.is_dir():
                        content += f"{newline}⚠️ Binärdatei/Unbekannte Datei ignoriert: {file_info.filename}{newline}"
                return {"type": "text", "content": content}
        except Exception as e:
            return {"type": "error", "content": f"Failed to process ZIP: {e}"}
    
    return {"type": "error", "content": "Unsupported file format"}


# --- Sidebar für Einstellungen ---
with st.sidebar:
    st.header("⚙️ API Settings")
    
    # API Key Management
    api_key = st.text_input("Google AI API Key", type="password")
    
    # Optimierte Modell-Liste
    model_list = [
        "gemini-2.5-flash", 
        "gemini-2.5-pro", 
        "gemini-1.5-flash", 
        "gemini-1.5-pro",
    ]
    
    model = st.selectbox("Model", model_list)
    
    st.caption("❗ Alle **2.5er** und **1.5er** Modelle sind **Vision-fähig** (Bilder, Dateien).")
    
    temperature = st.slider("Temperature", 0.0, 1.0, 0.7)
    max_tokens = st.slider("Max Tokens", 1, 100000, 1000)
    
    if st.button("🔄 Chat Reset (Full)"):
        st.session_state.messages = []
        st.session_state.uploaded_content = None
        st.experimental_rerun() 


# --- Datei Upload & Vorschau ---
uploaded_file = st.file_uploader("Upload File (Image/Text/PDF/ZIP)", 
                                 type=["jpg", "jpeg", "png", "txt", "pdf", "zip", "csv", "xlsx", "html", "css", "js", "py"])

if uploaded_file and st.session_state.uploaded_content is None:
    st.session_state.uploaded_content = process_file(uploaded_file)

if st.session_state.uploaded_content:
    processed = st.session_state.uploaded_content
    st.subheader("Current File Attachment:")
    
    if processed["type"] == "image":
        st.image(processed["content"], caption="Attached Image", width=300)
    elif processed["type"] == "text":
        st.text_area("File Preview", processed["content"], height=150)
    elif processed["type"] == "error":
        st.error(f"Error processing file: {processed['content']}")
        
    if st.button("❌ Clear Uploaded File Attachment"):
        st.session_state.uploaded_content = None
        st.experimental_rerun()


# --- Chat Verlauf anzeigen ---
for message in st.session_state.messages:
    # Anzeigen des reinen Textinhalts
    with st.chat_message(message["role"]):
        st.markdown(message["content"])

# --- Chat-Eingabe verarbeiten ---
if prompt := st.chat_input("Your message..."):
    if not api_key:
        st.warning("API Key benötigt!")
        st.stop()
    
    # 1. API konfigurieren
    genai.configure(api_key=api_key)
    model_instance = genai.GenerativeModel(model)

    # 2. History und neuen Content für den API-Call vorbereiten
    
    # Konvertiere die Streamlit-History in das Gemini-Format (role: user/model, parts: [{text: ...}, {image: ...}])
    contents = []
    for msg in st.session_state.messages:
        role_map = {"user": "user", "assistant": "model"}
        contents.append({"role": role_map.get(msg["role"]), "parts": [{"text": msg["content"]}]})
    
    
    # 3. Den neuen User-Prompt hinzufügen
    current_parts = [{"text": prompt}]
    
    # 4. Dateiinhalt hinzufügen (falls vorhanden)
    if st.session_state.uploaded_content:
        content_data = st.session_state.uploaded_content
        
        if content_data["type"] == "image":
            # Füge das PIL-Objekt direkt als Teil hinzu
            current_parts.append(content_data["content"])
        
        elif content_data["type"] == "text":
            # Füge den Text-Inhalt zum Prompt-Text hinzu
            current_parts[0]["text"] += f"\n\n[Attached File Content]\n{content_data['content']}"

    # Hinzufügen des vollständigen letzten User-Eintrags zum History-Array
    contents.append({"role": "user", "parts": current_parts})

    # 5. Nachricht zur Streamlit-Historie hinzufügen und anzeigen
    # Wir fügen den reinen Text-Prompt zur Streamlit-History hinzu, um die Darstellung einfach zu halten
    st.session_state.messages.append({"role": "user", "content": prompt})
    with st.chat_message("user"):
        st.markdown(prompt)

    # 6. Antwort generieren
    with st.spinner("Gemini is thinking..."):
        try:
            response = model_instance.generate_content(
                contents, # Das vollständige History-Array übergeben
                generation_config=genai.types.GenerateContentConfig(
                    temperature=temperature,
                    max_output_tokens=max_tokens
                )
            )
            
            response_text = response.text
            with st.chat_message("assistant"):
                st.markdown(response_text)
            st.session_state.messages.append({"role": "assistant", "content": response_text})
            
        except APIError as e:
            st.error(f"Gemini API Error: {str(e)}. Bitte prüfen Sie den API Key und die Modell-Wahl.")
        except Exception as e:
            st.error(f"General Error: {str(e)}")


# Instructions in the sidebar
with st.sidebar:
    st.markdown("""
    ---
    ## 📝 Instructions:
    1. Enter your **Google AI API Key**
    2. Select a **Gemini 2.5/1.5** model (all are multimodal)
    3. Adjust parameters (Temperature/Tokens)
    4. Upload a file (optional: **Image, Text, PDF, ZIP, CSV/XLSX**)
    5. Type your message and press Enter
    
    ### About
    🔗 [GitHub Profile](https://github.com/volkansah) | 📂 [Project Repository](https://github.com/volkansah/gemini-ai-chat)
    """)