Update app.py

app.py

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+import os
+import gradio as gr
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import plotly.graph_objects as go
+import requests
+import cv2
+from PIL import Image
+import qrcode
+from fpdf import FPDF
+from io import BytesIO
+from sentinelhub import SHConfig, MimeType, CRS, BBox, SentinelHubRequest, DataCollection
+from groq import Groq
+
+# -------------------- ENVIRONMENT VARIABLES --------------------
+HF_API_KEY = os.getenv("HF_API_KEY")
+GROQ_API_KEY = os.getenv("GROQ_API_KEY")
+DEEPSEEK_API_KEY = os.getenv("DEEPSEEK_API_KEY")
+SENTINEL_CLIENT_ID = os.getenv("SENTINEL_CLIENT_ID")
+SENTINEL_CLIENT_SECRET = os.getenv("SENTINEL_CLIENT_SECRET")
+
+# -------------------- SENTINEL HUB CONFIG --------------------
+config = SHConfig()
+config.client_id = SENTINEL_CLIENT_ID
+config.client_secret = SENTINEL_CLIENT_SECRET
+
+# -------------------- AI SUMMARY FUNCTIONS --------------------
+def hf_summary(text):
+    try:
+        url = "https://api-inference.huggingface.co/models/google/flan-t5-large"
+        headers = {"Authorization": f"Bearer {HF_API_KEY}"}
+        r = requests.post(url, headers=headers, json={"inputs": text}, timeout=20)
+        return r.json()[0]["generated_text"]
+    except:
+        return None
+
+def groq_summary(text):
+    try:
+        client = Groq(api_key=GROQ_API_KEY)
+        completion = client.chat.completions.create(
+            model="mixtral-8x7b-32768",
+            messages=[{"role": "user", "content": text}]
+        )
+        return completion.choices[0].message["content"]
+    except:
+        return None
+
+def deepseek_summary(text):
+    try:
+        url = "https://api.deepseek.com/v1/chat/completions"
+        headers = {
+            "Authorization": f"Bearer {DEEPSEEK_API_KEY}",
+            "Content-Type": "application/json"
+        }
+        payload = {
+            "model": "deepseek-chat",
+            "messages": [{"role": "user", "content": text}]
+        }
+        r = requests.post(url, headers=headers, json=payload, timeout=20)
+        return r.json()["choices"][0]["message"]["content"]
+    except:
+        return None
+
+def smart_summary(text):
+    if GROQ_API_KEY:
+        out = groq_summary(text)
+        if out: return out
+    if DEEPSEEK_API_KEY:
+        out = deepseek_summary(text)
+        if out: return out
+    if HF_API_KEY:
+        out = hf_summary(text)
+        if out: return out
+    return "⚠ No AI model available. Check API keys."
+
+# -------------------- WATER QUALITY CALCULATIONS --------------------
+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))
+
+# -------------------- SATELLITE IMAGE ANALYSIS --------------------
+def analyze_satellite_image(img):
+    img_array = np.array(img.convert("L"))
+    turbidity_score = int(np.mean(img_array)/2.55)  # scale 0-100
+    return turbidity_score
+
+# -------------------- PLOTLY CHARTS --------------------
+def create_plots(wqi, hsi, erosion, turbidity):
+    fig = go.Figure()
+    fig.add_trace(go.Bar(name="WQI", x=["WQI"], y=[wqi], marker_color='blue'))
+    fig.add_trace(go.Bar(name="HSI", x=["HSI"], y=[hsi], marker_color='green'))
+    fig.add_trace(go.Bar(name="Erosion", x=["Erosion"], y=[erosion], marker_color='red'))
+    fig.add_trace(go.Bar(name="Turbidity", x=["Turbidity"], y=[turbidity], marker_color='orange'))
+    fig.update_layout(title="River Health Metrics", barmode='group', yaxis=dict(range=[0,100]))
+    return fig
+
+# -------------------- PDF GENERATION --------------------
+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, align='C')
+    pdf.set_font("Arial", "", 12)
+    pdf.ln(10)
+    
+    # Metrics
+    pdf.cell(0,10,f"WQI Score: {wqi}", ln=True)
+    pdf.cell(0,10,f"HSI Score: {hsi}", ln=True)
+    pdf.cell(0,10,f"Erosion Potential: {erosion}", ln=True)
+    pdf.cell(0,10,f"Turbidity: {turbidity}", ln=True)
+    pdf.ln(10)
+    
+    # AI Summary + Biodiversity / Mitigation
+    pdf.multi_cell(0, 8, summary_text)
+    
+    # QR code
+    qr = qrcode.QRCode(box_size=4)
+    qr.add_data("FlumenIntel")
+    qr.make(fit=True)
+    img = qr.make_image(fill_color="black", back_color="white")
+    qr_buffer = BytesIO()
+    img.save(qr_buffer, format="PNG")
+    qr_buffer.seek(0)
+    pdf.image(qr_buffer, x=80, y=pdf.get_y(), w=50)
+    
+    output = BytesIO()
+    pdf.output(output)
+    output.seek(0)
+    return output
+
+# -------------------- MAIN FUNCTION --------------------
+def predict_river(flow_rate, water_temp, sediment, construction, pH, do, nutrients, sat_img):
+    try:
+        # Scores
+        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)
+        
+        # AI Summary Input for full report
+        summary_input = f"""
+Hydrological Data:
+- Flow rate: {flow_rate} m³/s
+- Water temperature: {water_temp} °C
+- Sediment: {sediment}
+- Construction: {construction}
+
+Chemical Data:
+- pH: {pH}
+- Dissolved Oxygen: {do} mg/L
+- Nutrient Load: {nutrients}
+
+Satellite Analysis:
+- Turbidity Score: {turbidity}
+
+Derived Scores:
+- WQI: {wqi}
+- HSI: {hsi}
+- Erosion: {erosion}
+- Potability: {potability}
+- Stability: {stability}
+
+Generate a **comprehensive environmental report** that includes:
+1. River health summary
+2. Potential impact on biodiversity and fish
+3. Recommended mitigation measures with explanation
+4. Suggested monitoring frequency
+"""
+        summary = smart_summary(summary_input)
+        
+        # Plot
+        fig = create_plots(wqi, hsi, erosion, turbidity)
+        
+        # PDF
+        pdf_file = generate_pdf(wqi, hsi, erosion, turbidity, summary)
+        
+        return f"River Stability: {stability}/100\nPotability: {potability}", fig, pdf_file, summary
+    except Exception as e:
+        return f"Error: {str(e)}", None, None, None
+
+# -------------------- GRADIO UI --------------------
+with gr.Blocks(title="FlumenIntel - River Health Predictor") as demo:
+    gr.Markdown("<h1 style='text-align:center;color:#1E90FF'>FlumenIntel 🌊</h1>", elem_id="title")
+    with gr.Row():
+        with gr.Column():
+            flow_rate = gr.Number(label="Flow Rate (m³/s)", value=50)
+            water_temp = gr.Number(label="Water Temperature (°C)", value=20)
+            sediment = gr.Number(label="Sediment Level", value=5)
+            construction = gr.Number(label="Construction Activity Level", value=2)
+            pH = gr.Number(label="pH Level", value=7)
+            do = gr.Number(label="Dissolved Oxygen (mg/L)", value=8)
+            nutrients = gr.Number(label="Nutrient Load (N+P)", value=3)
+            sat_img = gr.Image(label="Satellite Image (Upload or URL)", type="pil")
+            predict_btn = gr.Button("Predict River Health")
+        with gr.Column():
+            result_text = gr.Textbox(label="Predicted Output", interactive=False)
+            plot_output = gr.Plot(label="River Health Metrics")
+            pdf_output = gr.File(label="Download PDF Report")
+            ai_summary = gr.Textbox(label="AI Environmental Summary", interactive=False)
+    
+    predict_btn.click(
+        predict_river,
+        inputs=[flow_rate, water_temp, sediment, construction, pH, do, nutrients, sat_img],
+        outputs=[result_text, plot_output, pdf_output, ai_summary]
+    )
+
+# -------------------- CUSTOM CSS --------------------
+custom_css = """
+#title {background: linear-gradient(90deg, #1E90FF, #00CED1); padding: 20px; border-radius: 15px; color:white;}
+"""
+demo.launch(share=True, css=custom_css)