import praw
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import gradio as gr
import os

# Load your model
model_name = "amitkatoch/distilbertonsentiment"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
device = "cuda" if torch.cuda.is_available() else "cpu"
model = model.to(device)

# Load Reddit API keys from Hugging Face secrets
reddit = praw.Reddit(
    client_id=os.environ["REDDIT_CLIENT_ID"],
    client_secret=os.environ["REDDIT_CLIENT_SECRET"],
    user_agent=os.environ["REDDIT_USER_AGENT"]
)

def analyze_sentiment(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512).to(device)
    with torch.no_grad():
        outputs = model(**inputs)
    probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
    pred = torch.argmax(probs).item()
    confidence = probs[0][pred].item()
    return "😊 Positive" if pred == 1 else "😠 Negative", f"{confidence:.2%}"

def analyze_reddit(subreddit, num_posts=5):
    posts = []
    for submission in reddit.subreddit(subreddit).new(limit=num_posts):
        text = submission.title
        if submission.selftext:
            text += " " + submission.selftext[:200]
        sentiment, confidence = analyze_sentiment(text)
        posts.append({
            "Post": submission.title[:50] + "...",
            "Sentiment": sentiment,
            "Confidence": confidence,
            "Link": f"https://reddit.com{submission.permalink}"
        })
    return posts

# Gradio UI
with gr.Blocks() as app:
    gr.Markdown("# 🔍 Reddit Sentiment Analyzer")
    with gr.Row():
        subreddit = gr.Textbox(label="Subreddit (e.g., 'python')", value="all")
        num_posts = gr.Slider(1, 10, value=3, label="Number of Posts")
    btn = gr.Button("Analyze")
    output = gr.JSON(label="Results")
    btn.click(analyze_reddit, inputs=[subreddit, num_posts], outputs=output)

app.launch()