Create app.py

app.py

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+import streamlit as st
+import base64
+from openai import OpenAI
+from PIL import Image
+import io
+import cv2
+import numpy as np
+
+# Configure app
+st.set_page_config(
+    page_title="AI Vision Assistant",
+    page_icon="🔍",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# Custom CSS (keep your existing CSS here)
+st.markdown("""
+<style>
+    /* Your existing CSS styles */
+</style>
+""", unsafe_allow_html=True)
+
+# App title and description
+st.title("🔍 Optimus Alpha | Live Vision Assistant")
+
+# Initialize OpenAI client (keep your existing cached function)
+@st.cache_resource
+def get_client():
+    return OpenAI(
+        base_url="https://openrouter.ai/api/v1",
+        api_key='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'
+    )
+
+# ===== New Live Camera Section =====
+st.subheader("Live Camera Feed")
+run_camera = st.checkbox("Enable Camera", value=False)
+
+FRAME_WINDOW = st.empty()
+captured_image = None
+
+if run_camera:
+    cap = cv2.VideoCapture(0)
+    
+    capture_button = st.button("Capture Image")
+    stop_button = st.button("Stop Camera")
+    
+    if stop_button:
+        run_camera = False
+        cap.release()
+        st.experimental_rerun()
+    
+    while run_camera:
+        ret, frame = cap.read()
+        if not ret:
+            st.error("Failed to access camera")
+            break
+        
+        # Display the live feed
+        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        FRAME_WINDOW.image(frame)
+        
+        if capture_button:
+            captured_image = frame
+            run_camera = False
+            cap.release()
+            break
+else:
+    FRAME_WINDOW.info("Camera is currently off")
+
+# ===== Image Processing Section =====
+col1, col2 = st.columns([1, 2])
+
+with col1:
+    st.subheader("Image Source")
+    
+    # Option to use captured image or upload
+    if captured_image is not None:
+        st.image(captured_image, caption="Captured Image", width=300)
+        use_captured = True
+    else:
+        use_captured = False
+    
+    uploaded_file = st.file_uploader(
+        "Or upload an image",
+        type=["jpg", "jpeg", "png"],
+        disabled=use_captured
+    )
+    
+    # Determine which image to use
+    if use_captured:
+        image = Image.fromarray(captured_image)
+    elif uploaded_file:
+        image = Image.open(uploaded_file)
+    else:
+        image = None
+
+with col2:
+    st.subheader("AI Analysis")
+    
+    user_prompt = st.text_input(
+        "Your question about the image:",
+        placeholder="e.g. 'What objects do you see?' or 'Explain this diagram'",
+        key="user_prompt"
+    )
+    
+    if st.button("Analyze", type="primary") and image:
+        try:
+            # Convert image to base64
+            buffered = io.BytesIO()
+            image.save(buffered, format="JPEG")
+            image_base64 = base64.b64encode(buffered.getvalue()).decode("utf-8")
+            
+            # Prepare messages
+            messages = [
+                {
+                    "role": "system",
+                    "content": """You are a real-time vision assistant. Analyze the current camera feed or uploaded image and:
+1. Identify objects, people, text clearly
+2. Answer follow-up questions precisely
+3. Format responses with bullet points
+4. Highlight urgent/important findings"""
+                },
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "text",
+                            "text": user_prompt if user_prompt else "Describe what you see in detail"
+                        },
+                        {
+                            "type": "image_url",
+                            "image_url": {
+                                "url": f"data:image/jpeg;base64,{image_base64}"
+                            }
+                        }
+                    ]
+                }
+            ]
+            
+            # Stream the response
+            response_container = st.empty()
+            full_response = ""
+            
+            client = get_client()
+            stream = client.chat.completions.create(
+                model="openrouter/optimus-alpha",
+                messages=messages,
+                stream=True
+            )
+            
+            for chunk in stream:
+                if chunk.choices[0].delta.content is not None:
+                    full_response += chunk.choices[0].delta.content
+                    response_container.markdown(f"""
+                        <div class="markdown-text">
+                        {full_response}
+                        </div>
+                    """, unsafe_allow_html=True)
+        
+        except Exception as e:
+            st.error(f"Error: {str(e)}")
+
+# Sidebar (keep your existing sidebar)
+with st.sidebar:
+    st.image("blob.png", width=200)
+    st.markdown("""
+    *Powered by OpenRouter*
+    """)
+    st.markdown("---")
+    st.markdown("Made with ❤️ by Koshur AI")