Update app.py

app.py

@@ -7,25 +7,44 @@ import plotly.graph_objects as go
 
 def extract_scores_and_verdict(result_text):
     scores = {}
+
+    # Match categories based on keywords (not emojis)
     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
+        if match:
+            scores[key] = int(match.group(1))
+        else:
+            scores[key] = 0
+
+    # Match verdict based on emoji OR fallback to keyword
+    
+    #verdict_match = re.search(r"Verdict: (✅|⚠️|🚫)", result_text)
     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" }
+    values.append(values[0])  # close the loop for radar
+
+    colors = {
+        "✅": "green",
+        "⚠️": "orange",
+        "🚫": "red"
+    }
+
     fig = go.Figure()
+
     fig.add_trace(go.Scatterpolar(
         r=values,
         theta=categories + [categories[0]],
@@ -34,28 +53,65 @@ def generate_radar_plot(scores, verdict_emoji):
         line=dict(color=colors.get(verdict_emoji, "gray")),
         marker=dict(size=8)
     ))
+
     fig.update_layout(
-        polar=dict(radialaxis=dict(visible=True, range=[0, 5])),
+        polar=dict(
+            radialaxis=dict(visible=True, range=[0, 5]),
+        ),
         showlegend=False,
         title="🔎 Prompt Evaluation Radar"
     )
     return fig
 
+# Environment Setup
 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 = """You are a prompt evaluation assistant called PromptPolice...
-[unchanged template for brevity]
+# 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]"
 """
 
+# Function to query DeepSeek
 @st.cache_data(show_spinner=False)
 def evaluate_prompt(user_prompt):
     payload = {
-        "messages": [{"role": "user", "content": PROMPT_TEMPLATE.format(user_prompt=user_prompt)}],
+        "messages": [
+            {"role": "user", "content": PROMPT_TEMPLATE.format(user_prompt=user_prompt)}
+        ],
         "model": "deepseek/deepseek-r1-turbo",
-        "temperature": 0.7,
-        "stream": False
+        "temperature": 0.7,  # Required for HF's OpenAI-compatible endpoint
+        "stream": False      # Explicitly set streaming
     }
     response = requests.post(HF_API_URL, headers=HEADERS, json=payload)
     if response.status_code == 200:
@@ -63,10 +119,9 @@ def evaluate_prompt(user_prompt):
     else:
         return f"Error: {response.status_code} - {response.text}"
 
-# ----------- UI STARTS HERE -------------
-st.set_page_config("PromptPolice", page_icon="🚓", layout="wide")
-
-# Custom styling
+    
+# App UI
+st.set_page_config("PromptPolice", page_icon="🚓", layout="centered")
 st.markdown("""
     <style>
         .main {background-color: #0f1117; color: #f0f0f0; font-family: 'Segoe UI', sans-serif;}
@@ -79,39 +134,52 @@ st.markdown("""
 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.")
+# Sidebar
+with st.sidebar:
+    st.header("🛠 Prompt Tools")
+    use_example = st.toggle("Load Example Prompt")
+    st.markdown("---")
+    st.info("Paste a natural language prompt and get an instant evaluation. No fluff.", icon="📌")
+
+# Main Input
+if use_example:
+    user_input = st.text_area("Paste your prompt here:",
+        "Generate a short story about a robot in a post-apocalyptic world.",
+        height=200)
+else:
+    user_input = st.text_area("Paste your prompt here:", height=200)
+
+# Evaluate Button
+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)
 
-    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.markdown("---")
+        st.subheader(":clipboard: Evaluation Result")
+        scores, verdict_emoji = extract_scores_and_verdict(result)
+        radar_fig = generate_radar_plot(scores, verdict_emoji)
         st.plotly_chart(radar_fig, use_container_width=True)
 
+
+        # Add scroll anchor
         st.markdown("<a name='result'></a>", unsafe_allow_html=True)
-        st.subheader(":scroll: Evaluation Result")
 
+        # Render result with dark theme-friendly style
         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>
+                <pre style='white-space:pre-wrap; color:#f0f0f0;'>{result}</pre>
             </div>
         """, unsafe_allow_html=True)
 
-        st.download_button("Download Evaluation", st.session_state["result"], file_name="evaluation.txt")
+        # Download button
+        st.download_button("Download Evaluation", result, file_name="evaluation.txt")
+
+        # Toast + Balloons + Auto-scroll
         st.toast("✅ Evaluation complete!", icon="🤖")
         st.balloons()
         st.markdown("""
@@ -132,4 +200,4 @@ st.markdown("""
     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)
+""", unsafe_allow_html=True)