Create main.py

main.py

@@ -0,0 +1,160 @@
+import os
+import pickle
+import pandas as pd
+from fastapi import FastAPI, Request, HTTPException
+from fastapi.responses import HTMLResponse, JSONResponse
+from fastapi.templating import Jinja2Templates
+from fastapi.middleware.cors import CORSMiddleware
+from groq import Groq
+
+app = FastAPI(title="Student Score Predictor Chatbot + Groq")
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"], allow_credentials=True,
+    allow_methods=["*"],  allow_headers=["*"],
+)
+templates = Jinja2Templates(directory="templates")
+
+# ——— Load model at startup —————————————————————————————————————
+MODEL_PATH = os.getenv('MODEL_PATH', 'student_performance_model.pkl')
+try:
+    with open(MODEL_PATH, 'rb') as f:
+        model = pickle.load(f)
+except Exception as e:
+    raise RuntimeError(f"Could not load model: {e}")
+
+# ——— Load Groq API key —————————————————————————————————————————
+GROQ_API_KEY = os.getenv('GROQ_API_KEY')
+if not GROQ_API_KEY:
+    raise RuntimeError("Missing Groq API key. Set env var GROQ_API_KEY.")
+
+groq_client = Groq(api_key=GROQ_API_KEY)
+
+# ——— Chat‑fields configuration —————————————————————————————————
+FIELDS = [
+    {'name': 'Age', 'type': 'number',
+     'question': 'What is your age?',
+     'validation': {'min': 5, 'max': 100}},
+    {'name': 'Gender', 'type': 'select',
+     'question': 'What is your gender?',
+     'options': ['Male', 'Female', 'Other']},
+    {'name': 'HoursOfStudyPerDay', 'type': 'number',
+     'question': 'Hours of study per day?',
+     'validation': {'min': 0, 'max': 24}},
+    {'name': 'SchoolAttendanceRate', 'type': 'number',
+     'question': 'School attendance rate (%)?',
+     'validation': {'min': 0, 'max': 100}},
+    {'name': 'TuitionAccess', 'type': 'select',
+     'question': 'Access to extra tuition?',
+     'options': ['Yes', 'No']},
+    {'name': 'AveragePreviousScores', 'type': 'number',
+     'question': 'Average previous score?',
+     'validation': {'min': 0, 'max': 100}},
+    {'name': 'HoursOfSleep', 'type': 'number',
+     'question': 'Hours of sleep per night?',
+     'validation': {'min': 0, 'max': 24}},
+    {'name': 'BreakfastDaily', 'type': 'select',
+     'question': 'Do you eat breakfast daily?',
+     'options': ['Yes', 'No']},
+    {'name': 'ScreenTimeHours', 'type': 'number',
+     'question': 'Screen time hours per day?',
+     'validation': {'min': 0, 'max': 24}},
+    {'name': 'PhysicalActivityHours', 'type': 'number',
+     'question': 'Physical activity hours per day?',
+     'validation': {'min': 0, 'max': 24}},
+    {'name': 'PlaysSport', 'type': 'select',
+     'question': 'Do you play sports?',
+     'options': ['Yes', 'No']},
+    {'name': 'MentalHealthScore', 'type': 'number',
+     'question': 'Rate your mental health (1–10).',
+     'validation': {'min': 1, 'max': 10}},
+    {'name': 'ParentalEducationLevel', 'type': 'select',
+     'question': 'Parental education level?',
+     'options': ['High school', 'Graduate', 'Postgrad']},
+    {'name': 'HouseholdIncomeLevel', 'type': 'select',
+     'question': 'Household income level?',
+     'options': ['Low', 'Medium', 'High']},
+    {'name': 'StudyEnvironmentRating', 'type': 'number',
+     'question': 'Rate your study environment (1–5).',
+     'validation': {'min': 1, 'max': 5}},
+    {'name': 'FriendSupportScore', 'type': 'number',
+     'question': 'Friend support score (1–10).',
+     'validation': {'min': 1, 'max': 10}},
+    {'name': 'ParticipatesInClubs', 'type': 'select',
+     'question': 'Do you participate in clubs?',
+     'options': ['Yes', 'No']},
+    {'name': 'PartTimeWork', 'type': 'select',
+     'question': 'Do you do part‑time work?',
+     'options': ['Yes', 'No']},
+]
+
+@app.get("/", response_class=HTMLResponse)
+async def chat_ui(request: Request):
+    return templates.TemplateResponse("chat.html", {
+        "request": request,
+        "fields": FIELDS
+    })
+
+@app.post("/predict_json")
+async def predict_and_advise(payload: dict):
+    # — validate & cast —
+    data = {}
+    for f in FIELDS:
+        key = f["name"]
+        if key not in payload:
+            raise HTTPException(400, f"Missing field: {key}")
+        val = payload[key]
+        if f["type"] == "number":
+            try:
+                val = float(val)
+            except:
+                raise HTTPException(400, f"{key} must be numeric")
+        data[key] = val
+
+    # — range checks —
+    for f in FIELDS:
+        if f["type"] == "number" and "validation" in f:
+            mn, mx = f["validation"]["min"], f["validation"]["max"]
+            if not (mn <= data[f["name"]] <= mx):
+                raise HTTPException(400,
+                    f"{f['name']} must be between {mn} and {mx}")
+
+    # — predict score —
+    df = pd.DataFrame([data])
+    score = float(model.predict(df)[0])
+    data["PredictedScore"] = round(score, 2)
+
+    # — build Groq chat messages —
+    system_msg = {
+        "role": "system",
+        "content": (
+            "You are an expert academic coach. "
+            "Given a student’s profile data and their predicted final exam score, "
+            "provide a concise performance analysis and actionable improvement suggestions."
+        )
+    }
+    lines = [f"{k}: {v}" for k, v in data.items() if k != "PredictedScore"]
+    user_msg = {
+        "role": "user",
+        "content": (
+            "Here is the student data:\n" +
+            "\n".join(lines) +
+            f"\nPredicted final exam score: {data['PredictedScore']}\n"
+            "What targeted advice can you give them to improve their performance?"
+        )
+    }
+
+    # — call Groq —
+    resp = groq_client.chat.completions.create(
+        model="llama-3.3-70b-versatile",
+        messages=[system_msg, user_msg],
+        temperature=0.5,
+        max_completion_tokens=512,
+        top_p=1.0
+    )
+    advice = resp.choices[0].message.content
+
+    return JSONResponse({
+        "predicted": data["PredictedScore"],
+        "advice": advice
+    })