Update app.py

app.py

@@ -237,7 +237,7 @@ demo = gr.Interface(
 
 demo.launch()
 '''
-
+'''
 import gradio as gr
 from transformers import TFBertForSequenceClassification, BertTokenizer
 import tensorflow as tf
@@ -358,6 +358,155 @@ demo = gr.Interface(
 )
 
 demo.launch()
+'''
+import gradio as gr
+from transformers import TFBertForSequenceClassification, BertTokenizer
+import tensorflow as tf
+import praw
+import os
+import pytesseract
+from PIL import Image
+import numpy as np
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+from scipy.special import softmax
+import plotly.graph_objs as go
+
+# Install tesseract OCR (only runs once in Hugging Face Spaces)
+os.system("apt-get update && apt-get install -y tesseract-ocr")
+
+# Load main model
+model = TFBertForSequenceClassification.from_pretrained("shrish191/sentiment-bert")
+tokenizer = BertTokenizer.from_pretrained("shrish191/sentiment-bert")
+
+LABELS = {
+    0: "Neutral",
+    1: "Positive",
+    2: "Negative"
+}
+
+# Load fallback model
+fallback_model_name = "cardiffnlp/twitter-roberta-base-sentiment"
+fallback_tokenizer = AutoTokenizer.from_pretrained(fallback_model_name)
+fallback_model = AutoModelForSequenceClassification.from_pretrained(fallback_model_name)
+
+# Reddit API setup
+reddit = praw.Reddit(
+    client_id=os.getenv("REDDIT_CLIENT_ID"),
+    client_secret=os.getenv("REDDIT_CLIENT_SECRET"),
+    user_agent=os.getenv("REDDIT_USER_AGENT", "sentiment-classifier-ui")
+)
+
+def fetch_reddit_text(reddit_url):
+    try:
+        submission = reddit.submission(url=reddit_url)
+        return f"{submission.title}\n\n{submission.selftext}"
+    except Exception as e:
+        return f"Error fetching Reddit post: {str(e)}"
+
+def fetch_subreddit_texts(subreddit_name, limit=15):
+    texts = []
+    try:
+        subreddit = reddit.subreddit(subreddit_name)
+        for submission in subreddit.hot(limit=limit):
+            combined = f"{submission.title} {submission.selftext}".strip()
+            if combined:
+                texts.append(combined)
+        return texts
+    except Exception as e:
+        return [f"Error fetching subreddit: {str(e)}"]
+
+def fallback_classifier(text):
+    encoded_input = fallback_tokenizer(text, return_tensors='pt', truncation=True, padding=True)
+    with torch.no_grad():
+        output = fallback_model(**encoded_input)
+    scores = softmax(output.logits.numpy()[0])
+    labels = ['Negative', 'Neutral', 'Positive']
+    return f"Prediction: {labels[scores.argmax()]}"
+
+def classify_multiple_sentiments(texts):
+    counts = {"Positive": 0, "Neutral": 0, "Negative": 0}
+    for text in texts:
+        try:
+            inputs = tokenizer(text, return_tensors="tf", truncation=True, padding=True)
+            outputs = model(inputs)
+            probs = tf.nn.softmax(outputs.logits, axis=1)
+            confidence = float(tf.reduce_max(probs).numpy())
+            pred_label = tf.argmax(probs, axis=1).numpy()[0]
+
+            if confidence < 0.5:
+                label = fallback_classifier(text).split(":")[-1].strip()
+            else:
+                label = LABELS[pred_label]
+
+            counts[label] += 1
+        except:
+            continue
+    return counts
+
+def sentiment_pie_chart(counts):
+    labels = list(counts.keys())
+    values = list(counts.values())
+    fig = go.Figure(data=[go.Pie(labels=labels, values=values, hole=.3)])
+    fig.update_layout(title_text="Sentiment Distribution in Subreddit")
+    return fig
+
+def classify_sentiment(text_input, reddit_url, image, subreddit_name):
+    # Subreddit Dashboard has priority
+    if subreddit_name.strip():
+        texts = fetch_subreddit_texts(subreddit_name)
+        if "Error" in texts[0]:
+            return texts[0]
+        counts = classify_multiple_sentiments(texts)
+        return sentiment_pie_chart(counts)
+
+    if reddit_url.strip():
+        text = fetch_reddit_text(reddit_url)
+    elif image is not None:
+        try:
+            img_array = np.array(image)
+            gray = cv2.cvtColor(img_array, cv2.COLOR_RGB2GRAY)
+            thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
+            text = pytesseract.image_to_string(thresh)
+        except Exception as e:
+            return f"[!] OCR failed: {str(e)}"
+    elif text_input.strip():
+        text = text_input
+    else:
+        return "[!] Please enter some text, upload an image, or provide a Reddit URL."
+
+    if text.lower().startswith("error") or "Unable to extract" in text:
+        return f"[!] {text}"
+
+    try:
+        inputs = tokenizer(text, return_tensors="tf", truncation=True, padding=True)
+        outputs = model(inputs)
+        probs = tf.nn.softmax(outputs.logits, axis=1)
+        confidence = float(tf.reduce_max(probs).numpy())
+        pred_label = tf.argmax(probs, axis=1).numpy()[0]
+
+        if confidence < 0.5:
+            return fallback_classifier(text)
+
+        return f"Prediction: {LABELS[pred_label]}"
+    except Exception as e:
+        return f"[!] Prediction error: {str(e)}"
+
+# Gradio interface
+demo = gr.Interface(
+    fn=classify_sentiment,
+    inputs=[
+        gr.Textbox(label="Text Input (can be tweet or any content)", placeholder="Paste tweet or type any content here...", lines=4),
+        gr.Textbox(label="Reddit Post URL", placeholder="Paste a Reddit post URL (optional)", lines=1),
+        gr.Image(label="Upload Image (optional)", type="pil"),
+        gr.Textbox(label="Subreddit Name", placeholder="e.g. AskReddit (optional)", lines=1),
+    ],
+    outputs=gr.outputs.Component(label="Result"),
+    title="Sentiment Analyzer with Dashboard",
+    description="\ud83d\udd0d Paste any text, Reddit post URL, upload an image, or enter a subreddit name to analyze sentiment."
+)
+
+demo.launch()