Update app.py

app.py

@@ -11,100 +11,270 @@ from sentinelhub import SHConfig
 from groq import Groq
 import google.generativeai as genai
 import tempfile
-import pytz  # Ensure pytz is available
 
-# -------------------- API KEYS --------------------
+# -------------------- ENVIRONMENT VARIABLES --------------------
+HF_API_KEY = os.getenv("HF_API_KEY")
 GROQ_API_KEY = "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"
 GEMINI_API_KEY = "AIzaSyCqPnhDNwBP6Tsw1wkLGdXCIVDnNO44swY"
+SENTINEL_CLIENT_ID = os.getenv("SENTINEL_CLIENT_ID")
+SENTINEL_CLIENT_SECRET = os.getenv("SENTINEL_CLIENT_SECRET")
+
+# -------------------- SENTINEL CONFIG --------------------
+config = SHConfig()
+if SENTINEL_CLIENT_ID and SENTINEL_CLIENT_SECRET:
+    config.client_id = SENTINEL_CLIENT_ID
+    config.client_secret = SENTINEL_CLIENT_SECRET
 
 # -------------------- AI FUNCTIONS --------------------
-def smart_summary(text):
+def gemini_summary(text):
+    try:
+        if not GEMINI_API_KEY: return None, "Missing Key"
+        genai.configure(api_key=GEMINI_API_KEY)
+        model = genai.GenerativeModel('gemini-1.5-flash') 
+        response = model.generate_content(text)
+        return response.text, None
+    except Exception as e:
+        return None, str(e)
+
+def groq_summary(text):
     try:
+        if not GROQ_API_KEY: return None, "Missing Key"
         client = Groq(api_key=GROQ_API_KEY)
         completion = client.chat.completions.create(
             model="llama-3.3-70b-versatile",
             messages=[{"role": "user", "content": text}]
         )
-        return completion.choices[0].message.content
-    except:
-        try:
-            genai.configure(api_key=GEMINI_API_KEY)
-            model = genai.GenerativeModel('gemini-1.5-flash') 
-            response = model.generate_content(text)
-            return response.text
-        except:
-            return "⚠ AI Error: Check your internet and API keys."
-
-# -------------------- STABLE gTTS AUDIO --------------------
+        return completion.choices[0].message.content, None
+    except Exception as e:
+        return None, str(e)
+
+def hf_summary(text):
+    try:
+        url = "https://api-inference.huggingface.co/models/HuggingFaceH4/zephyr-7b-beta"
+        headers = {"Authorization": f"Bearer {HF_API_KEY}"}
+        payload = {
+            "inputs": f"<|system|>You are a scientist.</s><|user|>{text}</s><|assistant|>",
+            "parameters": {"max_new_tokens": 800}
+        }
+        r = requests.post(url, headers=headers, json=payload, timeout=25)
+        if r.status_code == 200:
+            return r.json()[0]["generated_text"].split("<|assistant|>")[-1], None
+        else:
+            return None, f"Status {r.status_code}: {r.text}"
+    except Exception as e:
+        return None, str(e)
+
+def smart_summary(text):
+    errors = []
+    out, err = groq_summary(text)
+    if out: return out
+    errors.append(f"Groq: {err}")
+    out, err = gemini_summary(text)
+    if out: return out
+    errors.append(f"Gemini: {err}")
+    if HF_API_KEY:
+        out, err = hf_summary(text)
+        if out: return out
+        errors.append(f"HF: {err}")
+    return "⚠ SYSTEM FAILURE. DEBUG LOG:\n" + "\n".join(errors)
+
+# -------------------- AUDIO FUNCTION (STABLE gTTS) --------------------
 def generate_audio_report(text):
     try:
         from gtts import gTTS
     except ImportError:
-        raise gr.Error("❌ Add 'gTTS' to requirements.txt")
+        raise gr.Error("❌ Library Missing! Add 'gTTS' to requirements.txt")
 
-    if not text or len(text.strip()) < 10:
-        raise gr.Error("❌ Generate the report first!")
+    if not text or "SYSTEM FAILURE" in text:
+        raise gr.Error("❌ No valid report text found. Generate report first!")
 
     try:
-        # Generate speech using free Google engine
+        # No API Key needed for gTTS
         tts = gTTS(text=text[:1500], lang='en')
         
         with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as f:
             tts.save(f.name)
             return f.name
     except Exception as e:
-        # Handles the 429 Too Many Requests error
         raise gr.Error(f"Speech Generation Error: {str(e)}")
 
-# -------------------- CORE LOGIC --------------------
-def run_app(flow, temp, sed, const, ph, do, nutri, sat_img):
-    wqi = max(0, min(100, int(((7 - abs(7 - ph)) * 0.2 + (do/14) * 0.5 + (10 - nutri) * 0.3) * 10)))
-    hsi = max(0, min(100, int(100 - abs(flow-50)*0.5 - abs(temp-20)*2 - sed*1.5)))
-    erosion = max(0, min(100, int(sed*1.5 + const*2)))
-    turb = int(np.mean(np.array(sat_img.convert("L")))/2.55) if sat_img else 0
-    
-    prompt = f"Scientist Report. WQI {wqi}, HSI {hsi}, Erosion {erosion}, Turbidity {turb}."
-    summary = smart_summary(prompt)
-    
-    fig = go.Figure(go.Bar(x=["WQI", "HSI", "Erosion", "Turbidity"], y=[wqi, hsi, erosion, turb], marker_color='#0061ff'))
-    fig.update_layout(yaxis=dict(range=[0,100]), template="plotly_white")
+# -------------------- MATH & LOGIC --------------------
+def calculate_wqi(pH, do, nutrients):
+    wqi = (7 - abs(7 - pH)) * 0.2 + (do/14) * 0.5 + (10 - nutrients) * 0.3
+    wqi_score = max(0, min(100, int(wqi*10)))
+    return wqi_score
+
+def calculate_hsi(flow_rate, temp, sediment):
+    hsi = 100 - abs(flow_rate-50)*0.5 - abs(temp-20)*2 - sediment*1.5
+    return max(0, min(100, int(hsi)))
+
+def calculate_erosion(sediment, construction):
+    score = sediment*1.5 + construction*2
+    return max(0, min(100, int(score)))
+
+def potability_status(wqi):
+    if wqi > 80: return "Safe"
+    elif wqi > 50: return "Boil Required"
+    else: return "Toxic"
+
+def river_stability(wqi, hsi, erosion):
+    return int((wqi*0.4 + hsi*0.4 + (100-erosion)*0.2))
+
+def analyze_satellite_image(img):
+    if img is None: return 0
+    img_array = np.array(img.convert("L"))
+    turbidity_score = int(np.mean(img_array)/2.55)
+    return turbidity_score
+
+# -------------------- VISUALS & INSIGHTS --------------------
+def create_plots(wqi, hsi, erosion, turbidity):
+    fig = go.Figure()
+    colors = ['#0061ff', '#60efff', '#ff4b4b', '#ffb347']
+    fig.add_trace(go.Bar(name="Metrics", x=["WQI", "HSI", "Erosion", "Turbidity"],
+                          y=[wqi, hsi, erosion, turbidity], marker_color=colors))
+    fig.update_layout(title="River Health Metrics", yaxis=dict(range=[0,100]), template="plotly_white")
+    return fig
+
+def generate_graph_insights(wqi, hsi, erosion, turbidity):
+    text = "### 📉 Graph Analysis\n\n"
+    if wqi > 70: text += f"🔵 **Water Quality:** {wqi}/100. Excellent condition.\n\n"
+    elif wqi > 40: text += f"🔵 **Water Quality:** {wqi}/100. Moderate pollution.\n\n"
+    else: text += f"🔵 **Water Quality:** {wqi}/100. **CRITICAL**.\n\n"
     
+    if hsi > 70: text += f"🟢 **Habitat:** {hsi}/100. Good biodiversity.\n\n"
+    else: text += f"🟢 **Habitat:** {hsi}/100. Poor conditions.\n\n"
+    return text
+
+# -------------------- PDF ENGINE --------------------
+def generate_pdf(wqi, hsi, erosion, turbidity, summary_text):
     pdf = FPDF()
     pdf.add_page()
-    pdf.set_font("Arial", "B", 16)
-    pdf.cell(0, 10, "FlumenIntel River Health Report", ln=True)
+    qr = qrcode.QRCode(box_size=3)
+    qr.add_data(f"Verified FlumenIntel Report | WQI: {wqi}")
+    qr.make(fit=True)
+    img = qr.make_image(fill_color="black", back_color="white")
+    
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as tmp:
+        img.save(tmp.name)
+        pdf.image(tmp.name, x=165, y=10, w=30)
+    
+    pdf.set_y(15)
+    pdf.set_font("Arial", "B", 24)
+    pdf.set_text_color(0, 97, 255)
+    pdf.cell(0, 10, "FlumenIntel", ln=True, align='L')
+    pdf.ln(10)
     pdf.set_font("Arial", "", 12)
-    pdf.multi_cell(0, 10, summary.encode('latin-1', 'replace').decode('latin-1'))
+    pdf.set_text_color(0, 0, 0)
+    
+    # Cleaning summary for FPDF compatibility
+    clean_text = summary_text.encode('latin-1', 'replace').decode('latin-1')
+    pdf.multi_cell(0, 6, clean_text)
     
     report_path = os.path.join(tempfile.gettempdir(), "FlumenIntel_Report.pdf")
     pdf.output(report_path)
-    return f"River Status: {wqi}/100", fig, summary, report_path
+    return report_path
+
+# -------------------- MAIN PROCESSOR --------------------
+def process_data(flow_rate, water_temp, sediment, construction, pH, do, nutrients, sat_img):
+    try:
+        wqi = calculate_wqi(pH, do, nutrients)
+        hsi = calculate_hsi(flow_rate, water_temp, sediment)
+        erosion = calculate_erosion(sediment, construction)
+        turbidity = analyze_satellite_image(sat_img)
+        stability = river_stability(wqi, hsi, erosion)
+        potability = potability_status(wqi)
+        
+        prompt = f"Write a professional health report for a river. WQI: {wqi}, HSI: {hsi}, Erosion: {erosion}, Turbidity: {turbidity}. Potability: {potability}."
+        summary = smart_summary(prompt)
+        
+        fig = create_plots(wqi, hsi, erosion, turbidity)
+        graph_text = generate_graph_insights(wqi, hsi, erosion, turbidity)
+        
+        # Returns path for gr.File to enable download
+        pdf_path = generate_pdf(wqi, hsi, erosion, turbidity, summary)
+        
+        status_text = f"Stability Index: {stability}/100\nStatus: {potability}"
+        return status_text, fig, graph_text, summary, pdf_path
+
+    except Exception as e:
+        return str(e), None, "", "", None
+
+# Wrapper
+def run_app(flow, temp, sediment, construction, ph, do, nutrients, sat_img):
+    return process_data(flow, temp, sediment, construction, ph, do, nutrients, sat_img)
+
+# -------------------- UI DESIGN --------------------
+custom_css = """
+@import url('https://fonts.googleapis.com/css2?family=Poppins:wght@300;400;600&display=swap');
+* { font-family: 'Poppins', sans-serif !important; }
+#title-box { background: linear-gradient(135deg, #0061ff 0%, #60efff 100%); color: white; padding: 20px; border-radius: 12px; text-align: center;}
+#analyze-btn { background: #0061ff; color: white; border: none; font-weight: bold; cursor: pointer; border-radius: 8px;}
+"""
 
-# -------------------- UI --------------------
 with gr.Blocks(title="FlumenIntel") as demo:
-    gr.Markdown("# FlumenIntel 🌊")
-    with gr.Row():
-        with gr.Column():
-            flow = gr.Number(label="Flow", value=45)
-            temp = gr.Number(label="Temp", value=18)
-            sed = gr.Slider(0, 10, label="Sediment", value=2)
-            const = gr.Slider(0, 10, label="Construction", value=0)
-            ph = gr.Number(label="pH", value=7.2)
-            do = gr.Number(label="Oxygen", value=9.5)
-            nutri = gr.Slider(0, 10, label="Nutrients", value=1)
-            sat_img = gr.Image(label="Satellite", type="pil")
-            btn = gr.Button("GENERATE REPORT", variant="primary")
-        with gr.Column():
-            status = gr.Textbox(label="Status")
-            plot = gr.Plot()
-            ai_txt = gr.Textbox(label="Report", lines=10)
+    gr.HTML(f"<style>{custom_css}</style>")
+    with gr.Column(elem_id="title-box"):
+        gr.Markdown("# FlumenIntel 🌊\n### Advanced River Health Analytics")
+
+    with gr.Tabs():
+        # --- TAB 1: DASHBOARD ---
+        with gr.TabItem("🚀 Dashboard"):
             with gr.Row():
-                audio_btn = gr.Button("🔊 Listen")
-                audio_out = gr.Audio(label="Audio")
-            pdf_out = gr.File(label="Download")
+                # LEFT INPUTS
+                with gr.Column(scale=1):
+                    gr.Markdown("### 1. Hydrological Data")
+                    flow = gr.Number(label="Flow Rate", value=45)
+                    temp = gr.Number(label="Temperature", value=18)
+                    sediment = gr.Slider(0, 10, label="Sediment", value=2)
+                    construction = gr.Slider(0, 10, label="Construction", value=0)
+                    
+                    gr.Markdown("### 2. Chemical Data")
+                    ph = gr.Number(label="pH Level", value=7.2)
+                    do = gr.Number(label="Dissolved Oxygen", value=9.5)
+                    nutrients = gr.Slider(0, 10, label="Nutrient Load", value=1)
+                    
+                    gr.Markdown("### 3. Visual Analysis")
+                    sat_img = gr.Image(label="Satellite Image", type="pil")
+                    
+                    analyze_btn = gr.Button("GENERATE REPORT", elem_id="analyze-btn")
+
+                # RIGHT OUTPUTS
+                with gr.Column(scale=2):
+                    status_box = gr.Textbox(label="System Status", interactive=False)
+                    
+                    with gr.Tabs():
+                        with gr.TabItem("📊 Visual Analytics"):
+                            plot_output = gr.Plot(label="Metric Visualization")
+                            graph_summary_box = gr.Markdown("### Insights...")
+                        
+                        with gr.TabItem("📄 Official Report"):
+                            ai_summary = gr.Textbox(label="Scientist's Assessment", lines=15, interactive=False)
+                            
+                            # --- AUDIO BUTTON ---
+                            with gr.Row():
+                                audio_btn = gr.Button("🔊 Listen to Report (gTTS)")
+                                audio_out = gr.Audio(label="Player", type="filepath")
+                            
+                            audio_btn.click(
+                                fn=generate_audio_report, 
+                                inputs=ai_summary, 
+                                outputs=audio_out
+                            )
+
+                        with gr.TabItem("📥 Export"):
+                            # The gr.File component provides the download sign automatically
+                            pdf_output = gr.File(label="Download Official FlumenIntel Report")
+
+        # --- TAB 2: ABOUT ME ---
+        with gr.TabItem("👤 About Me"):
+             gr.Markdown("## Abdullah\nComputer Engineering Undergraduate | AI & Hardware Enthusiast")
 
-    btn.click(run_app, [flow, temp, sed, const, ph, do, nutri, sat_img], [status, plot, ai_txt, pdf_out])
-    audio_btn.click(generate_audio_report, ai_txt, audio_out)
+    # Events
+    analyze_btn.click(
+        run_app,
+        inputs=[flow, temp, sediment, construction, ph, do, nutrients, sat_img],
+        outputs=[status_box, plot_output, graph_summary_box, ai_summary, pdf_output]
+    )
 
 if __name__ == "__main__":
     demo.launch()