Update app.py

app.py

@@ -10,7 +10,7 @@ from PIL import Image
 import qrcode
 from fpdf import FPDF
 from io import BytesIO
-from sentinelhub import SHConfig, MimeType, CRS, BBox, SentinelHubRequest, DataCollection
+from sentinelhub import SHConfig, SentinelHubRequest
 from groq import Groq
 
 # -------------------- ENVIRONMENT VARIABLES --------------------
@@ -21,9 +21,11 @@ SENTINEL_CLIENT_ID = os.getenv("SENTINEL_CLIENT_ID")
 SENTINEL_CLIENT_SECRET = os.getenv("SENTINEL_CLIENT_SECRET")
 
 # -------------------- SENTINEL HUB CONFIG --------------------
+# Only configure if keys are present to avoid startup crashes
 config = SHConfig()
-config.client_id = SENTINEL_CLIENT_ID
-config.client_secret = SENTINEL_CLIENT_SECRET
+if SENTINEL_CLIENT_ID and SENTINEL_CLIENT_SECRET:
+    config.client_id = SENTINEL_CLIENT_ID
+    config.client_secret = SENTINEL_CLIENT_SECRET
 
 # -------------------- AI SUMMARY FUNCTIONS --------------------
 def hf_summary(text):
@@ -37,17 +39,20 @@ def hf_summary(text):
 
 def groq_summary(text):
     try:
+        if not GROQ_API_KEY: return None
         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 completion.choices[0].message.content
+    except Exception as e:
+        print(f"Groq Error: {e}")
         return None
 
 def deepseek_summary(text):
     try:
+        if not DEEPSEEK_API_KEY: return None
         url = "https://api.deepseek.com/v1/chat/completions"
         headers = {
             "Authorization": f"Bearer {DEEPSEEK_API_KEY}",
@@ -63,6 +68,7 @@ def deepseek_summary(text):
         return None
 
 def smart_summary(text):
+    # Try Groq first (fastest/best for this), then DeepSeek, then HF
     if GROQ_API_KEY:
         out = groq_summary(text)
         if out: return out
@@ -72,7 +78,7 @@ def smart_summary(text):
     if HF_API_KEY:
         out = hf_summary(text)
         if out: return out
-    return "⚠ No AI model available. Check API keys."
+    return "⚠ No AI model available. Please check API keys in Settings."
 
 # -------------------- WATER QUALITY CALCULATIONS --------------------
 def calculate_wqi(pH, do, nutrients):
@@ -98,6 +104,8 @@ def river_stability(wqi, hsi, erosion):
 
 # -------------------- SATELLITE IMAGE ANALYSIS --------------------
 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)  # scale 0-100
     return turbidity_score
@@ -128,23 +136,42 @@ def generate_pdf(wqi, hsi, erosion, turbidity, summary_text):
     pdf.cell(0,10,f"Turbidity: {turbidity}", ln=True)
     pdf.ln(10)
     
-    # AI Summary + Biodiversity / Mitigation
-    pdf.multi_cell(0, 8, summary_text)
+    # AI Summary
+    # Handle encoding for PDF
+    safe_summary = summary_text.encode('latin-1', 'replace').decode('latin-1')
+    pdf.multi_cell(0, 8, safe_summary)
     
     # QR code
     qr = qrcode.QRCode(box_size=4)
-    qr.add_data("FlumenIntel")
+    qr.add_data("FlumenIntel Project")
     qr.make(fit=True)
     img = qr.make_image(fill_color="black", back_color="white")
+    
+    # Save QR to buffer
     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
+    # Embed QR in PDF
+    # Note: FPDF image handling with streams requires a temp file in some versions, 
+    # but let's try direct stream if supported, or save temp.
+    # Safe method for basic FPDF:
+    import tempfile
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as tmp:
+        img.save(tmp.name)
+        pdf.image(tmp.name, x=150, y=10, w=30)
+    
+    # Output PDF to bytes
+    # FPDF output returns a string in older versions, we convert to bytes
+    try:
+        pdf_bytes = pdf.output(dest='S').encode('latin-1')
+    except:
+        # Fallback for newer FPDF versions if installed
+        pdf_bytes = pdf.output(dest='S')
+        
+    output_buffer = BytesIO(pdf_bytes)
+    output_buffer.seek(0)
+    return output_buffer.getvalue() # Return bytes directly for Gradio
 
 # -------------------- MAIN FUNCTION --------------------
 def predict_river(flow_rate, water_temp, sediment, construction, pH, do, nutrients, sat_img):
@@ -157,50 +184,43 @@ def predict_river(flow_rate, water_temp, sediment, construction, pH, do, nutrien
         stability = river_stability(wqi, hsi, erosion)
         potability = potability_status(wqi)
         
-        # AI Summary Input for full report
+        # AI Summary Input
         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
-"""
+        River Analysis Data:
+        - WQI: {wqi} ({potability})
+        - Flow: {flow_rate} m3/s
+        - Erosion Risk: {erosion}
+        - Turbidity: {turbidity}
+        
+        Task: Write a short technical summary recommending specific mitigation for this river.
+        """
         summary = smart_summary(summary_input)
         
         # Plot
         fig = create_plots(wqi, hsi, erosion, turbidity)
         
-        # PDF
-        pdf_file = generate_pdf(wqi, hsi, erosion, turbidity, summary)
+        # PDF (Generate a temporary file path for Gradio to download)
+        pdf_bytes = generate_pdf(wqi, hsi, erosion, turbidity, summary)
         
-        return f"River Stability: {stability}/100\nPotability: {potability}", fig, pdf_file, summary
+        # Save to a temp file for Gradio to serve
+        import tempfile
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp_pdf:
+            tmp_pdf.write(pdf_bytes)
+            pdf_path = tmp_pdf.name
+        
+        return f"River Stability: {stability}/100\nPotability: {potability}", fig, pdf_path, summary
     except Exception as e:
-        return f"Error: {str(e)}", None, None, None
+        import traceback
+        return f"Error: {str(e)}\n{traceback.format_exc()}", 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")
+custom_css = """
+#title {background: linear-gradient(90deg, #1E90FF, #00CED1); padding: 20px; border-radius: 15px; color: white; text-align: center;}
+"""
+
+with gr.Blocks(title="FlumenIntel", css=custom_css) as demo:
+    gr.Markdown("<h1 id='title'>FlumenIntel 🌊</h1>")
+    
     with gr.Row():
         with gr.Column():
             flow_rate = gr.Number(label="Flow Rate (m³/s)", value=50)
@@ -210,11 +230,13 @@ with gr.Blocks(title="FlumenIntel - River Health Predictor") as demo:
             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")
+            sat_img = gr.Image(label="Satellite Image", type="pil")
+            predict_btn = gr.Button("Predict River Health", variant="primary")
+        
         with gr.Column():
             result_text = gr.Textbox(label="Predicted Output", interactive=False)
             plot_output = gr.Plot(label="River Health Metrics")
+            # PDF output must be type="filepath" for downloading
             pdf_output = gr.File(label="Download PDF Report")
             ai_summary = gr.Textbox(label="AI Environmental Summary", interactive=False)
     
@@ -224,8 +246,5 @@ with gr.Blocks(title="FlumenIntel - River Health Predictor") as demo:
         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)
+if __name__ == "__main__":
+    demo.launch()