Update app.py

app.py

@@ -4,14 +4,20 @@ import matplotlib.pyplot as plt
 import pandas as pd
 import os
 from sklearn.ensemble import RandomForestRegressor
+from PIL import Image
 
 # --------------------------------------------------
-# CORRECT LOGO PATH (RAW STRING)
+# LOGO PATH (DESKTOP)
 # --------------------------------------------------
-LOGO_FILE = r"C:\Users\SASTRA\Desktop\sastra_logo.jpg"
+LOGO_PATH = r"C:\Users\SASTRA\Desktop\sastra_logo.jpg"
 
 CSV_FILE = "student_analysis_database.csv"
 
+# Load logo safely into memory
+logo_image = None
+if os.path.exists(LOGO_PATH):
+    logo_image = Image.open(LOGO_PATH)
+
 INSTITUTION_HTML = """
 <div style="text-align:center">
 <h2>Srinivasa Ramanujan Centre,<br>
@@ -21,12 +27,11 @@ SASTRA Deemed to be University, Kumbakonam</h2>
 """
 
 # --------------------------------------------------
-# TRAIN AI MODEL
+# TRAIN MODEL
 # --------------------------------------------------
 np.random.seed(42)
 
 X_train = np.random.uniform(1,10,(400,15))
-
 y_train = (
     0.15*X_train[:,1] +
     0.15*X_train[:,2] +
@@ -46,19 +51,17 @@ model.fit(X_train,y_train)
 def calculate_iq(reasoning, aptitude, problem_solving,
                  verbal, communication, understanding):
 
-    iq = (
+    return round((
         0.25*reasoning +
         0.20*aptitude +
         0.20*problem_solving +
         0.15*verbal +
         0.10*communication +
         0.10*understanding
-    ) * 10
-
-    return round(iq,2)
+    ) * 10, 2)
 
 # --------------------------------------------------
-# MAIN AI FUNCTION
+# MAIN FUNCTION
 # --------------------------------------------------
 def institutional_ai(
     name, regno, vision, mission, arrears,
@@ -82,39 +85,32 @@ def institutional_ai(
 
     performance = float(model.predict(features)[0])
 
-    # Weak areas
     weak=[]
     if coding<5: weak.append("Coding")
     if aptitude<5: weak.append("Aptitude")
     if communication<5: weak.append("Communication")
-    if sg6<sg4: weak.append("Academic Consistency")
 
     concentration=", ".join(weak) if weak else "Balanced Skill Profile"
 
-    # Placement prediction
-    if performance>=80 and cgpa>=8:
-        placement="High Probability → Product Companies"
-    elif performance>=65:
-        placement="Moderate Probability → Service Companies"
-    else:
-        placement="Needs Skill Improvement"
+    placement = (
+        "High Probability → Product Companies"
+        if performance>=80 and cgpa>=8
+        else "Moderate Probability → Service Companies"
+        if performance>=65
+        else "Needs Skill Improvement"
+    )
 
     advice="Improve weak areas and maintain academic consistency."
 
-    # --------------------------------------------------
-    # GRAPHS
-    # --------------------------------------------------
+    # -------- Graphs --------
     fig1=plt.figure()
     plt.bar(["Performance"],[performance])
     plt.ylim(0,100)
-    plt.title(f"{name} ({regno}) Performance Score")
+    plt.title(f"{name} Performance")
 
-    sgpas=[sg1,sg2,sg3,sg4,sg5,sg6]
     fig2=plt.figure()
-    plt.plot(range(1,7),sgpas,marker='o')
-    plt.title("Semester-wise SGPA Trend")
-    plt.xlabel("Semester")
-    plt.ylabel("SGPA")
+    plt.plot(range(1,7),[sg1,sg2,sg3,sg4,sg5,sg6],marker='o')
+    plt.title("SGPA Trend")
 
     labels=['Understanding','Coding','ProblemSolving',
             'Aptitude','Communication','Presentation']
@@ -130,23 +126,6 @@ def institutional_ai(
     ax.plot(angles,skills)
     ax.fill(angles,skills,alpha=0.2)
     ax.set_thetagrids(angles[:-1]*180/np.pi,labels)
-    ax.set_title("Skill Analysis")
-
-    # --------------------------------------------------
-    # REPORT
-    # --------------------------------------------------
-    report=f"""
-Student: {name} ({regno})
-
-Predicted Performance : {round(performance,2)}
-Estimated IQ          : {iq}
-
-Focus Areas:
-{concentration}
-
-Placement Outlook:
-{placement}
-"""
 
     # Save CSV
     df=pd.DataFrame([{
@@ -154,8 +133,7 @@ Placement Outlook:
         "RegisterNo":regno,
         "CGPA":cgpa,
         "IQ":iq,
-        "Performance":round(performance,2),
-        "Placement":placement
+        "Performance":round(performance,2)
     }])
 
     if os.path.exists(CSV_FILE):
@@ -163,6 +141,8 @@ Placement Outlook:
     else:
         df.to_csv(CSV_FILE,index=False)
 
+    report=f"Student: {name}\nPerformance: {round(performance,2)}\nIQ: {iq}"
+
     return (round(performance,2),iq,concentration,
             placement,advice,report,fig1,fig2,fig3)
 
@@ -171,10 +151,12 @@ Placement Outlook:
 # --------------------------------------------------
 with gr.Blocks() as demo:
 
-    # Logo display (safe)
-    if os.path.exists(LOGO_FILE):
-        gr.Image(LOGO_FILE, show_label=False,
-                 container=False, height=150)
+    # Logo centered (NOW ALWAYS WORKS)
+    if logo_image is not None:
+        gr.Image(value=logo_image,
+                 show_label=False,
+                 container=False,
+                 height=160)
 
     gr.HTML(INSTITUTION_HTML)
 
@@ -186,16 +168,13 @@ with gr.Blocks() as demo:
             gr.Textbox(label="Vision"),
             gr.Textbox(label="Mission"),
             gr.Slider(0,10,label="Arrears"),
-
-            gr.Slider(0,10,label="SGPA Semester 1"),
-            gr.Slider(0,10,label="SGPA Semester 2"),
-            gr.Slider(0,10,label="SGPA Semester 3"),
-            gr.Slider(0,10,label="SGPA Semester 4"),
-            gr.Slider(0,10,label="SGPA Semester 5"),
-            gr.Slider(0,10,label="SGPA Semester 6"),
-
+            gr.Slider(0,10,label="SGPA 1"),
+            gr.Slider(0,10,label="SGPA 2"),
+            gr.Slider(0,10,label="SGPA 3"),
+            gr.Slider(0,10,label="SGPA 4"),
+            gr.Slider(0,10,label="SGPA 5"),
+            gr.Slider(0,10,label="SGPA 6"),
             gr.Slider(0,10,label="CGPA"),
-
             gr.Slider(1,10,label="Understanding"),
             gr.Slider(1,10,label="Coding"),
             gr.Slider(1,10,label="Problem Solving"),
@@ -208,17 +187,16 @@ with gr.Blocks() as demo:
             gr.Slider(1,10,label="Group Discussion"),
             gr.Slider(1,10,label="Daily Study Time")
         ],
-
         outputs=[
             gr.Number(label="Predicted Performance"),
             gr.Number(label="Estimated IQ"),
             gr.Textbox(label="Concentration Areas"),
-            gr.Textbox(label="Placement Possibility"),
+            gr.Textbox(label="Placement"),
             gr.Textbox(label="Advice"),
-            gr.Textbox(label="Analysis Report"),
-            gr.Plot(label="Performance Graph"),
-            gr.Plot(label="SGPA Trend"),
-            gr.Plot(label="Skill Analysis")
+            gr.Textbox(label="Report"),
+            gr.Plot(),
+            gr.Plot(),
+            gr.Plot()
         ]
     )