import streamlit as st
import base64
import requests
import json
import io
import re
from PIL import Image

st.set_page_config(page_title="Solar Rooftop Analyzer", layout="centered")
st.title("\U0001F31E Solar Rooftop Analysis")

st.markdown("Upload a rooftop image and provide your location and budget. The system will analyze the rooftop and estimate potential solar installation ROI.")

OPENROUTER_API_KEY = "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"
VISION_MODEL_NAME = "opengvlab/internvl3-14b:free"

def analyze_image_with_openrouter(image_file):
    # Read and convert image to JPEG bytes
    img = Image.open(image_file).convert("RGB")
    buffer = io.BytesIO()
    img.save(buffer, format="JPEG")
    jpeg_bytes = buffer.getvalue()
    
    # Base64 encode with content-type prefix
    encoded_image = "data:image/jpeg;base64," + base64.b64encode(jpeg_bytes).decode("utf-8")
    
    prompt = (
        "Analyze the rooftop in this image. Output JSON with: "
        "[Roof area (sqm), Sunlight availability (%), Shading (Yes/No), "
        "Recommended solar panel type, Estimated capacity (kW)]."
    )
    
    headers = {
        "Authorization": f"Bearer {OPENROUTER_API_KEY}",
        "Content-Type": "application/json"
    }
    
    payload = {
        "model": VISION_MODEL_NAME,
        "messages": [
            {
                "role": "user",
                "content": [
                    {"type": "text", "text": prompt},
                    {"type": "image_url", "image_url": {"url": encoded_image}}
                ]
            }
        ]
    }
    
    response = requests.post("https://openrouter.ai/api/v1/chat/completions", json=payload, headers=headers)
    
    if response.status_code == 200:
        return response.json()
    
    return {"error": f"Failed to analyze image. Status code: {response.status_code}, Response: {response.text}"}

def extract_json_from_response(content):
    try:
        match = re.search(r"\{.*\}", content, re.DOTALL)
        if match:
            return json.loads(match.group(0))
    except Exception as e:
        st.warning(f"Failed to parse JSON: {e}")
    return None

def estimate_roi(roof_area, capacity_kw, budget):
    cost_per_kw = 65000  # INR/kW
    estimated_cost = capacity_kw * cost_per_kw
    incentives = estimated_cost * 0.30
    net_cost = estimated_cost - incentives
    annual_savings = capacity_kw * 1500 * 7
    payback_years = round(net_cost / annual_savings, 2)
    return {
        "estimated_cost": estimated_cost,
        "incentives": incentives,
        "net_cost": net_cost,
        "annual_savings": annual_savings,
        "payback_years": payback_years,
        "within_budget": budget >= net_cost
    }

with st.form("solar_form"):
    uploaded_file = st.file_uploader("Upload Rooftop Image", type=["jpg", "jpeg", "png"])
    location = st.text_input("Location")
    budget = st.number_input("Budget (INR)", min_value=10000.0, step=1000.0)
    submitted = st.form_submit_button("Analyze")

if submitted:
    if uploaded_file and location and budget:
        st.image(uploaded_file, caption="Uploaded Rooftop Image", use_column_width=True)
        with st.spinner("Analyzing rooftop image..."):
            ai_response = analyze_image_with_openrouter(uploaded_file)

        if "choices" in ai_response:
            try:
                content = ai_response["choices"][0]["message"]["content"]
                content_json = extract_json_from_response(content)
                
                if content_json:
                    st.success("Analysis complete!")
                    st.subheader("Rooftop Analysis")
                    st.json(content_json)

                    if "Roof area (sqm)" in content_json and "Estimated capacity (kW)" in content_json:
                        roi = estimate_roi(
                            roof_area=content_json["Roof area (sqm)"],
                            capacity_kw=content_json["Estimated capacity (kW)"],
                            budget=budget
                        )
                        st.subheader("ROI Estimation")
                        st.json(roi)
                else:
                    st.error("Could not extract structured data from the AI response.")
                    st.text(content)  # Fallback: show raw content
            except Exception as e:
                st.error(f"Error parsing analysis content: {e}")
                st.json(ai_response)
        else:
            st.error("Failed to analyze the image. Please try again.")
    else:
        st.warning("Please upload an image and fill all fields.")