app.py

import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from lime.lime_text import LimeTextExplainer
from groq import Groq
from PIL import Image
from sentence_transformers import CLIPModel, CLIPProcessor
import tempfile
import json
import os

# ==== Load BERT Fake News Model ====
model_name = "mrm8488/bert-tiny-fake-news"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
model.eval()

# ==== Load CLIP for Image-Text Similarity ====
clip_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")

# ==== LIME Explainer ====
explainer = LimeTextExplainer(class_names=["Real", "Fake"])

# ==== Groq Client ====
client = Groq(api_key=os.environ.get("GROQ_API_KEY"))

# ==== News Classification ====
def classify_news(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
    with torch.no_grad():
        outputs = model(**inputs)
        probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
    return probs[0].tolist()

# ==== LIME Explanation ====
def explain_prediction(text):
    def predictor(texts):
        return [classify_news(t) for t in texts]
    explanation = explainer.explain_instance(text, predictor, num_features=6)
    return explanation.as_list()

# ==== LLM Second Opinion ====
def get_llm_opinion(text):
    try:
        response = client.chat.completions.create(
            model="mixtral-8x7b-32768",
            messages=[
                {"role": "system", "content": "You are a helpful assistant that detects fake news based on user input."},
                {"role": "user", "content": f"Please evaluate the following news and tell if it's fake or real, and why:\n\n{text}"}
            ]
        )
        return response.choices[0].message.content.strip()
    except Exception as e:
        return f"LLM Error: {str(e)}"

# ==== CLIP Image-Text Similarity ====
def verify_image_with_text(image, text):
    if image is None:
        return "No image uploaded."
    inputs = clip_processor(text=[text], images=image, return_tensors="pt", padding=True)
    outputs = clip_model(**inputs)
    logits_per_image = outputs.logits_per_image
    probs = logits_per_image.softmax(dim=1)
    similarity = probs[0][0].item()
    return f"Image-Headline Similarity Score: {similarity:.2f}"

# ==== Main Pipeline ====
def analyze_news(text, image=None):
    prediction = classify_news(text)
    model_label = "Real" if prediction[0] > prediction[1] else "Fake"
    
    lime_output = explain_prediction(text)
    lime_expl = [(word, round(weight, 3)) for word, weight in lime_output]

    llm_verdict = get_llm_opinion(text)
    img_verification = verify_image_with_text(image, text)

    report = {
        "model_prediction": model_label,
        "bert_probs": {"Real": round(prediction[0], 3), "Fake": round(prediction[1], 3)},
        "lime_explanation": lime_expl,
        "llm_opinion": llm_verdict,
        "image_text_similarity": img_verification
    }

    with tempfile.NamedTemporaryFile(delete=False, suffix=".json", mode="w") as tmp:
        json.dump(report, tmp, indent=2)
        json_path = tmp.name

    return model_label, lime_expl, llm_verdict, img_verification, json_path

# ==== Gradio UI ====
demo = gr.Interface(
    fn=analyze_news,
    inputs=[
        gr.Textbox(label="News Text", lines=5, placeholder="Paste news text here..."),
        gr.Image(label="Optional Image")
    ],
    outputs=[
        gr.Text(label="BERT Model Verdict"),
        gr.HighlightedText(label="LIME Explanation"),
        gr.Text(label="Groq LLM Opinion"),
        gr.Text(label="Image-Text Match Score"),
        gr.File(label="Download JSON Report")
    ],
    title="📰 AI Fake News Detector with BERT + LIME + Groq LLM",
    description="Upload news text (and optional image). Compare BERT model prediction with Groq LLM’s second opinion. Get LIME explanations, CLIP-based image-text match score, and export full report as JSON."
)

if __name__ == "__main__":
    demo.launch()