Create app.py
Browse files
app.py
ADDED
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import streamlit as st
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import google.generativeai as genai
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from PIL import Image
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import io
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import base64
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import pandas as pd
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import zipfile
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import PyPDF2
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st.set_page_config(page_title="Gemini AI Chat", layout="wide")
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st.title("🤖 Gemini AI Chat Interface")
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st.markdown("""
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**Welcome to the Gemini AI Chat Interface!**
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Chat seamlessly with Google's advanced Gemini AI models, supporting multiple input types.
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🔗 [GitHub Profile](https://github.com/volkansah) |
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📂 [Project Repository](https://github.com/volkansah/gemini-ai-chat) |
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💬 [Soon](https://aicodecraft.io)
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""")
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# Session State Management
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if "messages" not in st.session_state:
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st.session_state.messages = []
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if "uploaded_content" not in st.session_state:
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st.session_state.uploaded_content = None
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# File Processing Functions
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def encode_image(image):
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buffered = io.BytesIO()
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image.save(buffered, format="JPEG")
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return base64.b64encode(buffered.getvalue()).decode('utf-8')
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def process_file(uploaded_file):
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file_type = uploaded_file.name.split('.')[-1].lower()
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if file_type in ["jpg", "jpeg", "png"]:
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return {"type": "image", "content": Image.open(uploaded_file).convert('RGB')}
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if file_type in ["txt", *code_extensions := ["html", "css", "php", "js", "py", "java", "c", "cpp"]]:
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return {"type": "text", "content": uploaded_file.read().decode("utf-8")}
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if file_type in ["csv", "xlsx"]:
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df = pd.read_csv(uploaded_file) if file_type == "csv" else pd.read_excel(uploaded_file)
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return {"type": "text", "content": df.to_string()}
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if file_type == "pdf":
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reader = PyPDF2.PdfReader(uploaded_file)
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return {"type": "text", "content": "".join(page.extract_text() for page in reader.pages if page.extract_text())}
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if file_type == "zip":
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with zipfile.ZipFile(uploaded_file) as z:
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return {"type": "text", "content": f"ZIP Contents:\n{'\n'.join(z.namelist())}"}
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return {"type": "error", "content": "Unsupported file format"}
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# Sidebar Configuration
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with st.sidebar:
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api_key = st.text_input("Google AI API Key", type="password")
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model = st.selectbox("Model", [
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"gemini-1.5-flash",
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"gemini-1.5-pro",
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"gemini-1.5-flash-8B",
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"gemini-1.5-pro-vision-latest",
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"gemini-1.0-pro",
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"gemini-1.0-pro-vision-latest",
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"gemini-2.0-pro-exp-02-05",
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"gemini-2.0-flash-lite",
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"gemini-2.0-flash-exp-image-generation",
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"gemini-2.0-flash",
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"gemini-2.0-flash-thinking-exp-01-21"
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])
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temperature = st.slider("Temperature", 0.0, 1.0, 0.7)
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max_tokens = st.slider("Max Tokens", 1, 2048, 1000)
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# File Upload Section
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uploaded_file = st.file_uploader("Upload File (Image/Text/PDF/ZIP)",
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type=["jpg", "jpeg", "png", "txt", "pdf", "zip",
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"csv", "xlsx", "html", "css", "php", "js", "py"])
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if uploaded_file:
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processed = process_file(uploaded_file)
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st.session_state.uploaded_content = processed
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if processed["type"] == "image":
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st.image(processed["content"], caption="Uploaded Image", use_container_width=True)
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elif processed["type"] == "text":
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st.text_area("File Preview", processed["content"], height=200)
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# Chat History Display
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for message in st.session_state.messages:
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with st.chat_message(message["role"]):
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st.markdown(message["content"])
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# Chat Input Processing
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if prompt := st.chat_input("Your message..."):
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if not api_key:
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st.warning("API Key benötigt!")
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st.stop()
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try:
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# Configure Gemini
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genai.configure(api_key=api_key)
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model_instance = genai.GenerativeModel(model)
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# Build content payload
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content = []
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# Add text input
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content.append({"text": prompt})
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# Add file content
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if st.session_state.uploaded_content:
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if st.session_state.uploaded_content["type"] == "image":
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content.append({
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"inline_data": {
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"mime_type": "image/jpeg",
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"data": encode_image(st.session_state.uploaded_content["content"])
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}
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})
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elif st.session_state.uploaded_content["type"] == "text":
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content[0]["text"] += f"\n\n[File Content]\n{st.session_state.uploaded_content['content']}"
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# Add to chat history
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st.session_state.messages.append({"role": "user", "content": prompt})
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with st.chat_message("user"):
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st.markdown(prompt)
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# Generate response
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response = model_instance.generate_content(
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content,
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generation_config=genai.types.GenerationConfig(
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temperature=temperature,
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max_output_tokens=max_tokens
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)
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)
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# Display response
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with st.chat_message("assistant"):
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st.markdown(response.text)
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st.session_state.messages.append({"role": "assistant", "content": response.text})
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except Exception as e:
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st.error(f"API Error: {str(e)}")
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if "vision" not in model and st.session_state.uploaded_content["type"] == "image":
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st.error("Für Bilder einen Vision-fähigen Modell auswählen!")
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