Update app.py

app.py

@@ -5,160 +5,142 @@ import plotly.express as px
 import qrcode
 from io import BytesIO
 
-# --- CALCULATION LOGIC --- #
-def calculate_risk(water, land, noise):
-    score = 0
-
-    # Water Quality
-    if water == "Clean":
-        score += 1
-    elif water == "Moderate":
-        score += 2
-    else:
-        score += 3
-
-    # Land Disturbance
-    if land == "Low":
-        score += 1
-    elif land == "Medium":
-        score += 2
-    else:
-        score += 3
-
-    # Noise Level
-    if noise == "Low":
-        score += 1
-    elif noise == "Medium":
-        score += 2
-    else:
-        score += 3
-
-    if score <= 4:
-        return score, "Low Risk"
-    elif score <= 6:
-        return score, "Moderate Risk"
-    else:
-        return score, "High Risk"
+# ================================================================
+# Helper Function: Species Summary + Map + QR Code
+# ================================================================
 
+def process_species_data(species_name, habitat, water_disturbance, land_disturbance, noise_level):
+    if not species_name:
+        return "Please enter a species name.", None, None
 
-# --- MAIN PROCESSING FUNCTION --- #
-def analyze_biodiversity(water, land, noise, species, habitat):
-
-    # ------- Fetch Species Summary (Wikipedia API) ------- #
+    # ======================
+    # 1. Fetch Wikipedia Summary (Full Extract)
+    # ======================
     wiki_url = "https://en.wikipedia.org/w/api.php"
     params = {
         "action": "query",
         "format": "json",
         "prop": "extracts",
-        "titles": species.replace(" ", "_"),
-        "explaintext": True,
+        "titles": species_name.replace(" ", "_"),
+        "explaintext": True
     }
-    res = requests.get(wiki_url, params=params).json()
-    pages = res.get("query", {}).get("pages", {})
+
+    response = requests.get(wiki_url, params=params).json()
+    pages = response.get("query", {}).get("pages", {})
     page = next(iter(pages.values()))
-    extract = page.get("extract", "")
 
-    # Ensure 200+ words
+    extract = page.get("extract", "No summary available for this species.")
+
+    # Ensure at least 200 words
     words = extract.split()
     if len(words) < 200:
-        extract = extract + " " + (
-            "This species plays an essential ecological role in its habitat. "
-            "Environmental pressures such as water pollution, land disturbance, and noise "
-            "can significantly affect its survival and reproductive behavior. "
-        ) * 10
-
-    summary = " ".join(extract.split()[:350])
-
-
-    # ------- Calculate Impact Score ------- #
-    score, level = calculate_risk(water, land, noise)
-
-    impact_text = f"""
-### 🌿 Environmental Impact Assessment  
-**Species:** {species}  
-**Habitat:** {habitat}  
-
-The environmental parameters you entered indicate a **{level}** biodiversity risk level (Score: {score}/9).  
-Water quality, land disturbance, and noise levels directly influence population survival, reproduction, food availability, and mobility of **{species}**.  
-
-Based on the current inputs, the habitat stability for this species is **{level.lower()}**, meaning that conservation attention is **{'critical' if level=='High Risk' else 'recommended'}**.
-
-This assessment is combined with scientific information retrieved from public ecological databases.
+        extract += "\n\n" + (
+            "This species plays a crucial ecological role, influencing biodiversity, habitat structure, "
+            "and environmental balance. Its presence often reflects the health of local ecosystems, and "
+            "changes in population trends can indicate environmental stress. Conservation efforts are "
+            "frequently focused on such species due to their importance in maintaining natural stability. "
+            * 3
+        )
+
+    summary = " ".join(words[:350])  # around 300–350 words
+
+    # Add environmental interpretation
+    summary += f"""
+
+Environmental Impact Assessment (Based on User Inputs):
+- Water Disturbance Level: {water_disturbance}/100
+- Land Disturbance Level: {land_disturbance}/100
+- Noise Level: {noise_level}/100
+- Habitat Type: {habitat}
+
+Higher disturbance values indicate increased pressure on this species' survival and reproduction.
 """
 
+    # ======================
+    # 2. Fetch GBIF Occurrence Data (Map API)
+    # ======================
+    gbif_url = f"https://api.gbif.org/v1/occurrence/search?scientificName={species_name}&limit=200"
+    gbif_res = requests.get(gbif_url).json()
+
+    results = gbif_res.get("results", [])
+    coords = []
 
-    # ------- Fetch Occurrence Map (GBIF API) ------- #
-    gbif_url = f"https://api.gbif.org/v1/occurrence/search?scientificName={species}&limit=200"
-    gbif_data = requests.get(gbif_url).json()
-    records = gbif_data.get("results", [])
+    for r in results:
+        lat = r.get("decimalLatitude")
+        lon = r.get("decimalLongitude")
+        if lat and lon:
+            coords.append({"lat": lat, "lon": lon})
 
-    if records:
-        df = pd.DataFrame([
-            {"lat": rec.get("decimalLatitude"), "lon": rec.get("decimalLongitude")}
-            for rec in records if rec.get("decimalLatitude") and rec.get("decimalLongitude")
-        ])
-        fig = px.scatter_mapbox(df, lat="lat", lon="lon", height=380, zoom=1)
+    if coords:
+        df = pd.DataFrame(coords)
+        fig = px.scatter_mapbox(df, lat="lat", lon="lon", zoom=1, height=400)
         fig.update_layout(mapbox_style="open-street-map")
     else:
         fig = None
 
-
-    # ------- QR Code ------- #
+    # ======================
+    # 3. QR Code Generator
+    # ======================
+    qr_data = f"BioVigilus Report\nSpecies: {species_name}\nHabitat: {habitat}\nSummary: {summary[:150]}..."
+    
     qr_img = BytesIO()
-    qr_data = f"BioVigilus Report\nSpecies: {species}\nHabitat: {habitat}\nRisk Level: {level}"
-    qrcode.make(qr_data).save(qr_img)
+    qr = qrcode.make(qr_data)
+    qr.save(qr_img)
     qr_img.seek(0)
 
+    return summary, fig, qr_img
 
-    # Final combined output
-    full_summary = impact_text + "\n\n### 📘 Species Summary\n" + summary
-
-    return full_summary, fig, qr_img
-
-
-
-
-# ---------------- UI (BEAUTIFUL) ---------------- #
+# ================================================================
+# Custom CSS for Beautiful UI
+# ================================================================
 
 css = """
-body {background:#E8F6F3;}
-.gr-box {border-radius:20px; box-shadow:0 4px 15px rgba(0,0,0,0.1); padding:15px;}
-.gr-button {background:#1B7F5F; color:white; border-radius:14px; font-weight:bold;}
-.gr-button:hover {background:#26A27D;}
+body { background-color: #f2f7f4 !important; }
+.gradio-container { font-family: 'Poppins', sans-serif; }
+
+h1 {
+    color: #2e7d32 !important;
+    font-weight: 700;
+}
+
+.custom-box {
+    background: white;
+    padding: 20px;
+    border-radius: 15px;
+    box-shadow: 0 4px 15px rgba(0,0,0,0.1);
+}
 """
 
-with gr.Blocks(css=css) as demo:
-    gr.Markdown("<h1 style='text-align:center;color:#1B7F5F;'>🌱 BioVigilus</h1>")
-    gr.Markdown("<p style='text-align:center;color:#444;'>Latin Name • Environmental Biodiversity Intelligence</p>")
+# ================================================================
+# Gradio UI (Corrected for v4)
+# ================================================================
 
-    with gr.Box():
-        gr.Markdown("## Enter Environmental Parameters")
+with gr.Blocks() as demo:
+    demo.load_css(css)
 
-        with gr.Row():
-            water = gr.Dropdown(["Clean", "Moderate", "Polluted"], label="Water Quality")
-            land = gr.Dropdown(["Low", "Medium", "High"], label="Land Disturbance")
-            noise = gr.Dropdown(["Low", "Medium", "High"], label="Noise Level")
+    gr.Markdown("<h1 style='text-align:center;'>🌿 BioVigilus — Biodiversity Impact Analyzer</h1>")
+    gr.Markdown("<p style='text-align:center; font-size:18px;'>Analyze species, habitats, and environmental stress factors with real-time data.</p>")
 
-        with gr.Row():
-            species = gr.Textbox(label="Species Name", placeholder="e.g., Panthera tigris")
-            habitat = gr.Textbox(label="Habitat Type", placeholder="e.g., Tropical Forest, Grassland")
+    with gr.Row():
+        with gr.Column(elem_classes="custom-box"):
+            species_name = gr.Textbox(label="Species Name", placeholder="e.g., Panthera tigris")
+            habitat = gr.Textbox(label="Habitat Type", placeholder="Forest / Grassland / Wetland")
 
-        process = gr.Button("Generate Biodiversity Analysis")
+            water = gr.Slider(0, 100, label="Water Disturbance Level")
+            land = gr.Slider(0, 100, label="Land Disturbance Level")
+            noise = gr.Slider(0, 100, label="Noise Level")
 
-    with gr.Row():
-        with gr.Column(scale=3):
-            full_out = gr.Markdown(label="Full Environmental + Species Report")
-
-        with gr.Column(scale=2):
-            map_out = gr.Plot(label="Species Distribution Map")
-        
-        with gr.Column(scale=1):
-            qr_out = gr.Image(label="QR Code")
-
-    process.click(
-        analyze_biodiversity,
-        inputs=[water, land, noise, species, habitat],
-        outputs=[full_out, map_out, qr_out]
+            analyze_btn = gr.Button("Analyze", variant="primary")
+
+        with gr.Column(elem_classes="custom-box"):
+            summary_output = gr.Textbox(label="Species Summary", lines=15)
+            map_output = gr.Plot(label="Distribution Map")
+            qr_output = gr.Image(label="QR Code")
+
+    analyze_btn.click(
+        process_species_data,
+        inputs=[species_name, habitat, water, land, noise],
+        outputs=[summary_output, map_output, qr_output]
     )
 
 demo.launch()