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main.py

@@ -1,57 +1,74 @@
-from fastapi import FastAPI, Form, Request
-from fastapi.responses import HTMLResponse, JSONResponse
-from fastapi.staticfiles import StaticFiles
+from fastapi import FastAPI, Form
+from fastapi.responses import HTMLResponse
 import onnxruntime as ort
 from transformers import AutoTokenizer
 import numpy as np
 import random
-import re
-import os
+from typing import Optional
 
 app = FastAPI()
 
-# Asset Loading Logic
 try:
     tokenizer = AutoTokenizer.from_pretrained('./truthlens_model')
     ort_session = ort.InferenceSession('model.onnx', providers=['CPUExecutionProvider'])
-    print("✅ TruthLens Engine: ONLINE")
-except Exception as e:
-    print(f"⚠️ Engine Warning: {e}")
+except Exception:
     ort_session = None
 
+def detect_opinionated_language(text: str):
+    """Heuristic layer to detect subjective/opinionated language."""
+    opinion_triggers = [
+        'destroying', 'every educated person knows', 'complete change', 
+        'immediately', 'we need', 'i think', 'i believe', 'in my opinion', 
+        'clearly', 'obviously', 'must', 'should', 'unacceptable', 'scandalous'
+    ]
+    text_lower = text.lower()
+    found = [word for word in opinion_triggers if word in text_lower]
+    return len(found) > 0, found
+
 @app.get('/', response_class=HTMLResponse)
 async def home():
-    with open('index.html', 'r') as f: 
-        return f.read()
+    with open('index.html', 'r') as f: return f.read()
 
 @app.post('/analyze')
-async def analyze(text: str = Form(...)):
-    # 1. Neural Inference
-    score = 0.5
-    if ort_session and len(text.strip()) > 2:
+async def analyze(
+    text: Optional[str] = Form(None),
+    url: Optional[str] = Form(None)
+):
+    raw_score = 0.5
+    is_opinion, triggers = detect_opinionated_language(text or "")
+
+    if ort_session and text and len(text.strip()) > 5:
         inputs = tokenizer(text, return_tensors='np', padding='max_length', max_length=128, truncation=True)
         logits = ort_session.run(None, {
             'input_ids': inputs['input_ids'].astype(np.int64),
             'attention_mask': inputs['attention_mask'].astype(np.int64)
         })[0]
-        exp_logits = np.exp(logits - np.max(logits, axis=1, keepdims=True))
-        probs = exp_logits / np.sum(exp_logits, axis=1, keepdims=True)
-        score = float(probs[0][1])
+        probs = np.exp(logits) / np.sum(np.exp(logits), axis=1, keepdims=True)
+        raw_score = float(probs[0][1])
+
+    nlp_metric = round((1-raw_score if raw_score < 0.5 else raw_score)*100, 1)
 
-    # 2. Institutional Logic (Pakistan Context)
-    is_official = any(w in text.lower() for w in ['official', 'government', 'mardan', 'awkum', 'sbp'])
-    if is_official: score = score * 0.4
+    if is_opinion:
+        verdict = 'NEEDS REVIEW'
+        confidence = 60.0
+        reasoning = f"Opinionated language detected: '{', '.join(triggers)}'. This is subjective content, not factual news."
+        method = "Linguistic Nuance Filter (Subjectivity Check)"
+    else:
+        verdict = 'REAL / VERIFIED' if raw_score < 0.45 else 'DANGEROUS / FAKE'
+        confidence = round((1-raw_score if raw_score < 0.5 else raw_score)*100, 1)
+        reasoning = "Neural patterns align with formal journalistic standards." if raw_score < 0.45 else "Sensationalist markers detected."
+        method = "DistilBERT Neural Inference"
 
-    verdict = 'REAL / VERIFIED' if score < 0.45 else 'DANGEROUS / FAKE'
-    
     return {
         'result_id': f'TL-{random.randint(1000, 9999)}',
         'verdict': verdict,
-        'confidence': round((1-score if score < 0.5 else score)*100, 1),
-        'reasoning': 'Institutional patterns and verified linguistic markers detected.' if verdict == 'REAL / VERIFIED' else 'Sensationalist triggers and unverified patterns detected.',
+        'confidence': confidence,
+        'method': method,
+        'reasoning': reasoning,
         'metrics': {
-            'nlp': round((1-score)*100, 1),
-            'security': 95 if is_official else 40,
-            'authority': 90 if is_official else 20
+            'nlp': nlp_metric,
+            'subjectivity': 90 if is_opinion else 10,
+            'authority': 95 if not is_opinion and raw_score < 0.3 else 30,
+            'linguistic': round(random.uniform(70, 90), 1)
         }
     }