# app.py
import streamlit as st
import requests
import os
import re
import plotly.graph_objects as go

def extract_scores_and_verdict(result_text):
    scores = {}
    patterns = {
        "Clarity": r"[-–•\s]*[^\w]?Clarity:\s*(\d)/5",
        "Context": r"[-–•\s]*[^\w]?Context:\s*(\d)/5",
        "Specificity": r"[-–•\s]*[^\w]?Specificity:\s*(\d)/5",
        "Intent Alignment": r"[-–•\s]*[^\w]?Intent Alignment:\s*(\d)/5"
    }
    for key, pattern in patterns.items():
        match = re.search(pattern, result_text)
        scores[key] = int(match.group(1)) if match else 0
    verdict_match = re.search(r"Verdict:\s*(✅|⚠️|🚫)", result_text)
    verdict_emoji = verdict_match.group(1) if verdict_match else "✅"
    return scores, verdict_emoji

def generate_radar_plot(scores, verdict_emoji):
    categories = list(scores.keys())
    values = list(scores.values())
    values.append(values[0])  # loop
    colors = { "✅": "green", "⚠️": "orange", "🚫": "red" }
    fig = go.Figure()
    fig.add_trace(go.Scatterpolar(
        r=values,
        theta=categories + [categories[0]],
        fill='toself',
        name='Prompt Score',
        line=dict(color=colors.get(verdict_emoji, "gray")),
        marker=dict(size=8)
    ))
    fig.update_layout(
        polar=dict(radialaxis=dict(visible=True, range=[0, 5])),
        showlegend=False,
        title="🔎 Prompt Evaluation Radar"
    )
    return fig

HF_API_URL = "https://router.huggingface.co/novita/v3/openai/chat/completions"
HF_TOKEN = os.environ.get("HF_PROJECT_TOKEN")
HEADERS = {"Authorization": f"Bearer {HF_TOKEN}"}

# Prompt Template
PROMPT_TEMPLATE = """
You are a prompt evaluation assistant called PromptPolice. Evaluate the following user prompt based on the criteria below. For each, rate from 1 (poor) to 5 (excellent), explain why, and suggest specific improvements.

Prompt:

{user_prompt} 

Evaluation Criteria (1–5):
- 🔍 Clarity: Is the prompt easy to understand?
- 🧠 Context: Does the prompt provide enough background or situational framing?
- 🎯 Specificity: Are the goals or constraints clearly defined?
- 🛍️ Intent Alignment: Is it clear what task or behavior the prompt is meant to elicit?
Verdict Logic:
- ✅ **Pass**: Overall Score is 16 or above AND no criterion rated below 3.
- ⚠️ **Warning**: Score between 11–15 OR one rating below 3.
- 🚫 **Ticket**: Score 10 or below OR two or more ratings below 3.
Output Format:
Evaluation:
- 🔍 Clarity: X/5 — [Explanation]
- 🧠 Context: X/5 — [Explanation]
- 🎯 Specificity: X/5 — [Explanation]
- 🛏️ Intent Alignment: X/5 — [Explanation]
Overall Score: X/20
Flaw Summary: [One-line summary of the weakest point]
🛡️ Verdict: [✅ Pass / ⚠️ Warning / 🚫 Ticket] — [Justification based on score and flaw]
Prompt Type(s): [e.g., Instruction, Summarization, Query, Roleplay, Classification, Creative Writing, Other]
Suggestions:
- [Actionable suggestion 1]
- [Actionable suggestion 2]
Improved Prompt:
"[Rewritten version of the user prompt]"
"""


@st.cache_data(show_spinner=False)
def evaluate_prompt(user_prompt):
    payload = {
        "messages": [{"role": "user", "content": PROMPT_TEMPLATE.format(user_prompt=user_prompt)}],
        "model": "deepseek/deepseek-r1-turbo",
        "temperature": 0.7,
        "stream": False
    }
    response = requests.post(HF_API_URL, headers=HEADERS, json=payload)
    if response.status_code == 200:
        return response.json()["choices"][0]["message"]["content"]
    else:
        return f"Error: {response.status_code} - {response.text}"

# ----------- UI STARTS HERE -------------
st.set_page_config("PromptPolice", page_icon="🚓", layout="wide")

# Custom styling
st.markdown("""
    <style>
        .main {background-color: #0f1117; color: #f0f0f0; font-family: 'Segoe UI', sans-serif;}
        textarea, input, .stTextArea textarea {background-color: #1e1e1e; color: #f0f0f0; border-radius: 10px;}
        .stButton>button {background-color: #2c2f3a; color: white; border-radius: 10px;}
        .stDownloadButton>button {background-color: #40434f; color: white; border-radius: 10px;}
    </style>
""", unsafe_allow_html=True)

st.title("🚓 PromptPolice")
st.caption("Evaluate your prompts like a seasoned detective.")

col1, col2 = st.columns([1, 2], gap="large")

with col1:
    user_input = st.text_area("Paste your prompt here:", height=300, placeholder="e.g. Generate a short story about a robot in a post-apocalyptic world.")

    if st.button(":mag_right: Evaluate Prompt"):
        if not HF_TOKEN:
            st.error("Missing Hugging Face token. Please set HF_PROJECT_TOKEN as environment variable.")
        elif user_input.strip() == "":
            st.warning("Please enter a prompt to evaluate.")
        else:
            with st.spinner("Evaluating prompt with PromptPolice..."):
                result = evaluate_prompt(user_input)
            st.session_state["result"] = result
            st.session_state["scores"], st.session_state["verdict"] = extract_scores_and_verdict(result)

# Right column — show result only if evaluated
if "result" in st.session_state:
    with col2:
        st.subheader(":bar_chart: Radar Score")
        radar_fig = generate_radar_plot(st.session_state["scores"], st.session_state["verdict"])
        st.plotly_chart(radar_fig, use_container_width=True)

        st.markdown("<a name='result'></a>", unsafe_allow_html=True)
        st.subheader(":scroll: Evaluation Result")

        st.markdown(f"""
            <div style='background-color:#1e1e1e; color:#f0f0f0; padding:20px; border-radius:10px; border: 1px solid #444; font-family: monospace; font-size: 15px;'>
                <pre style='white-space:pre-wrap; color:#f0f0f0;'>{st.session_state["result"]}</pre>
            </div>
        """, unsafe_allow_html=True)

        st.download_button("Download Evaluation", st.session_state["result"], file_name="evaluation.txt")
        st.toast("✅ Evaluation complete!", icon="🤖")
        st.balloons()
        st.markdown("""
            <script>
                const resultAnchor = document.querySelector("a[name='result']");
                if (resultAnchor) {
                    setTimeout(() => {
                        resultAnchor.scrollIntoView({ behavior: 'smooth' });
                    }, 100);
                }
            </script>
        """, unsafe_allow_html=True)

# Footer
st.markdown("""
---
<center>
    Made with ❤️ by <b>Penguins</b> · Powered by <code>DeepSeek R1 Turbo</code><br>
    No data stored · No nonsense · Just prompt justice ⚖️
</center>
""", unsafe_allow_html=True)