Update app.py

app.py

@@ -361,69 +361,134 @@ demo.launch()
 '''
 
 import gradio as gr
-import praw
-import pandas as pd
-import plotly.graph_objs as go
-from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
-from tensorflow.nn import softmax
+from transformers import TFAutoModelForSequenceClassification, AutoTokenizer
+import tensorflow as tf
 import numpy as np
+import praw
+import re
+from wordcloud import WordCloud
+import matplotlib.pyplot as plt
+from collections import Counter
+import plotly.graph_objects as go
+import os
 
-# Load model and tokenizer
-model_name = "shrish191/sentiment-bert"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = TFAutoModelForSequenceClassification.from_pretrained(model_name)
+# Load pre-trained model and tokenizer
+model = TFAutoModelForSequenceClassification.from_pretrained("shrish191/sentiment-bert")
+tokenizer = AutoTokenizer.from_pretrained("shrish191/sentiment-bert")
 
-def classify_sentiment(text):
-    inputs = tokenizer(text, return_tensors="tf", padding=True, truncation=True)
-    outputs = model(inputs)
-    scores = softmax(outputs.logits, axis=1).numpy()[0]
-    labels = ['Negative', 'Neutral', 'Positive']
-    sentiment = labels[np.argmax(scores)]
-    confidence = round(float(np.max(scores)) * 100, 2)
-    return sentiment, confidence
+label_map = {0: 'Negative', 1: 'Neutral', 2: 'Positive'}
 
-# Reddit sentiment dashboard
-reddit = praw.Reddit(
-    client_id="YOUR_CLIENT_ID",
-    client_secret="YOUR_CLIENT_SECRET",
-    user_agent="YOUR_USER_AGENT"
-)
+# Sentiment Prediction Function
+def predict_sentiment(text):
+    inputs = tokenizer(text, return_tensors="tf", truncation=True, padding=True)
+    outputs = model(inputs)[0]
+    probs = tf.nn.softmax(outputs, axis=1).numpy()
+    pred_label = np.argmax(probs, axis=1)[0]
+    return label_map[pred_label]
+
+# Reddit URL Handling
+def analyze_reddit_url(url):
+    reddit = praw.Reddit(
+        client_id="YOUR_CLIENT_ID",
+        client_secret="YOUR_CLIENT_SECRET",
+        user_agent="YOUR_USER_AGENT"
+    )
+    try:
+        submission = reddit.submission(url=url)
+        submission.comments.replace_more(limit=0)
+        comments = [comment.body for comment in submission.comments.list() if len(comment.body) > 10][:100]
+        sentiments = [predict_sentiment(comment) for comment in comments]
+        sentiment_counts = Counter(sentiments)
+        result_text = "\n".join([f"{s}: {c}" for s, c in sentiment_counts.items()])
+
+        # Pie chart
+        fig = go.Figure(data=[go.Pie(labels=list(sentiment_counts.keys()),
+                                     values=list(sentiment_counts.values()),
+                                     hole=0.3)])
+        fig.update_layout(title="Sentiment Distribution of Reddit Comments")
+        return result_text, fig
+    except Exception as e:
+        return str(e), None
+
+# Subreddit Analysis Function
+def analyze_subreddit(subreddit_name):
+    reddit = praw.Reddit(
+        client_id="YOUR_CLIENT_ID",
+        client_secret="YOUR_CLIENT_SECRET",
+        user_agent="YOUR_USER_AGENT"
+    )
+    try:
+        subreddit = reddit.subreddit(subreddit_name)
+        posts = list(subreddit.hot(limit=100))
+        texts = [post.title + " " + post.selftext for post in posts if post.selftext or post.title]
+
+        if not texts:
+            return "No valid text data found in subreddit.", None
 
-def analyze_subreddit(subreddit_name, num_posts):
-    posts = []
-    for submission in reddit.subreddit(subreddit_name).hot(limit=num_posts):
-        if not submission.stickied:
-            sentiment, confidence = classify_sentiment(submission.title)
-            posts.append({"title": submission.title, "sentiment": sentiment, "confidence": confidence})
+        sentiments = [predict_sentiment(text) for text in texts]
+        sentiment_counts = Counter(sentiments)
+        result_text = "\n".join([f"{s}: {c}" for s, c in sentiment_counts.items()])
 
-    df = pd.DataFrame(posts)
-    sentiment_counts = df['sentiment'].value_counts().reindex(['Positive', 'Neutral', 'Negative'], fill_value=0)
-    total = sentiment_counts.sum()
-    sentiment_percentages = (sentiment_counts / total * 100).round(2)
+        # Pie chart
+        fig = go.Figure(data=[go.Pie(labels=list(sentiment_counts.keys()),
+                                     values=list(sentiment_counts.values()),
+                                     hole=0.3)])
+        fig.update_layout(title=f"Sentiment Distribution in r/{subreddit_name}")
 
-    fig = go.Figure(data=[
-        go.Pie(labels=sentiment_percentages.index, values=sentiment_percentages.values, hole=.4)
-    ])
-    fig.update_layout(title="Sentiment Distribution in r/{} ({} posts)".format(subreddit_name, num_posts))
+        return result_text, fig
+    except Exception as e:
+        return str(e), None
 
-    return df, fig
+# Image Upload Functionality
+from PIL import Image
+import pytesseract
 
+def extract_text_from_image(image):
+    try:
+        img = Image.open(image)
+        text = pytesseract.image_to_string(img)
+        return text
+    except Exception as e:
+        return f"Error extracting text: {e}"
+
+def analyze_image_sentiment(image):
+    extracted_text = extract_text_from_image(image)
+    if extracted_text:
+        sentiment = predict_sentiment(extracted_text)
+        return f"Extracted Text: {extracted_text}\n\nPredicted Sentiment: {sentiment}"
+    return "No text extracted."
+
+# Gradio Interface
 with gr.Blocks() as demo:
-    gr.Markdown("## Reddit Subreddit Sentiment Dashboard")
-    subreddit_input = gr.Textbox(label="Enter Subreddit (without r/)", placeholder="e.g., technology")
-    num_posts_input = gr.Slider(10, 100, step=10, value=30, label="Number of Posts to Analyze")
-    analyze_button = gr.Button("Analyze")
-    sentiment_table = gr.Dataframe(label="Post Sentiments")
-    sentiment_chart = gr.Plot(label="Sentiment Pie Chart")
-
-    analyze_button.click(
-        analyze_subreddit,
-        inputs=[subreddit_input, num_posts_input],
-        outputs=[sentiment_table, sentiment_chart]
-    )
+    gr.Markdown("## 🧠 Sentiment Analysis App")
+    with gr.Tab("Analyze Text"):
+        input_text = gr.Textbox(label="Enter text")
+        output_text = gr.Textbox(label="Predicted Sentiment")
+        analyze_btn = gr.Button("Analyze")
+        analyze_btn.click(fn=predict_sentiment, inputs=input_text, outputs=output_text)
+
+    with gr.Tab("Analyze Reddit URL"):
+        reddit_url = gr.Textbox(label="Enter Reddit post URL")
+        url_result = gr.Textbox(label="Sentiment Counts")
+        url_plot = gr.Plot(label="Pie Chart")
+        analyze_url_btn = gr.Button("Analyze Reddit Comments")
+        analyze_url_btn.click(fn=analyze_reddit_url, inputs=reddit_url, outputs=[url_result, url_plot])
+
+    with gr.Tab("Analyze Image"):
+        image_input = gr.Image(label="Upload an image")
+        image_result = gr.Textbox(label="Sentiment from Image Text")
+        analyze_img_btn = gr.Button("Analyze Image")
+        analyze_img_btn.click(fn=analyze_image_sentiment, inputs=image_input, outputs=image_result)
+
+    with gr.Tab("Analyze Subreddit"):
+        subreddit_input = gr.Textbox(label="Enter subreddit name (without r/)")
+        subreddit_result = gr.Textbox(label="Sentiment Counts")
+        subreddit_plot = gr.Plot(label="Pie Chart")
+        analyze_subreddit_btn = gr.Button("Analyze Subreddit")
+        analyze_subreddit_btn.click(fn=analyze_subreddit, inputs=subreddit_input, outputs=[subreddit_result, subreddit_plot])
+
+demo.launch()
 
-if __name__ == "__main__":
-    demo.launch()