Update api.py

api.py

@@ -1,206 +1,208 @@
-from flask import Flask, request, jsonify, send_file
-from bs4 import BeautifulSoup
-from newspaper import Article
-from textblob import TextBlob
-# from newsapi import NewsApiClient
-from transformers import pipeline
-import requests
-from utils import *
-import pandas as pd
-import base64
-import json
-# import nest_asyncio
-
-app = Flask(__name__)
-
-# newsapi = NewsApiClient(api_key='YOUR_NEWS_API_KEY')  # Replace with your API key
-
-# @app.route('/analyze_news', methods=['GET'])
-# def analyze_news():
-def analyze_news(company, source):
-    company = company
-    source = source
-    # company = request.args.get('company')
-    # source = request.args.get('source')
-    if not company or not source:
-        return jsonify({"error": "Please provide a company name as a query parameter"}), 400
-    
-    all_articles = []
-    output = {"Company": f"{company}", "Articles": all_articles}
-    
-    overall_sentiment_count = 0
-    sentiment_count = {"POSITIVE": 0, "NEGATIVE": 0, "NEUTRAL": 0}
-
-    if source == "NewsOrg":
-        # Fetch articles from News API
-        # response = newsapi.get_everything(q=company, page_size=5, sort_by='publishedAt', language='en')
-
-        params = {"q":"tesla","apiKey":"7396bdb0bc0a42c5b5b0c9c5945d32fa", "pagesize":10, "sortBy": "publishedAt", "language":'en'}
-        articles = requests.get(url = "https://newsapi.org/v2/everything", params= params)
-        articles = json.loads(articles.text)
-
-        # results = []
-        # sentiment_count = {"POSITIVE": 0, "NEGATIVE": 0, "NEUTRAL": 0}
-
-        # print(f">>>>>>>>>>>>>>>>>>>>>{articles}")
-        for idx, article in enumerate(articles["articles"]):
-            # print(f">>>>>>>>>>>>>>>>>>>>>{article}")
-            url = article.get("url")
-            news_article = Article(url)
-            try:
-                news_article.download()
-                news_article.parse()
-            except:
-                continue
-
-            blob = TextBlob(news_article.text)
-            polarity = blob.sentiment.polarity
-
-            if polarity > 0.3:
-                sentiment = "POSITIVE"
-                overall_sentiment_count += 1
-            elif polarity < -0.3:
-                sentiment = "NEGATIVE"
-                overall_sentiment_count -= 1
-            else:
-                sentiment = "NEUTRAL"
-                # neutral_sentiment_count += 1
-            
-            sentiment_count[sentiment] += 1
-
-            all_articles.append({
-                "Title": article.get("title"),
-                "Summary": article.get("description"),
-                "Sentiment": sentiment
-            })
-
-        output["Comparitive Sentiment Score"] = {
-            "Sentiment Distribution": sentiment_count
-        }
-
-        if overall_sentiment_count>0:
-            output["Final Sentiment Analysis"] = f"{company.capitalize()}'s lastest news is mostly positive. Potential stock growth expected."
-        elif overall_sentiment_count<0:
-            output["Final Sentiment Analysis"] = f"{company.capitalize()}'s lastest news is mostly negative. Potential stock decline expected."
-        else:
-            output["Final Sentiment Analysis"] = f"{company.capitalize()}'s lastest news is mostly neutral. Stocks going to stay stagnant for some time."
-        
-        print(output)
-        print(f"{'>'*5} Starting text summarization.")
-
-        # return jsonify({
-        #     "company": company,
-        #     "sentiment_distribution": sentiment_count,
-        #     "articles": results
-        # })
-        # df = pd.DataFrame(all_articles)
-        text_to_summarize = " ".join([d['Title'] + " " + d['Summary'] for d in all_articles[:5]])
-        summary_final = summarize_text(text_to_summarize)
-
-        audio_path = generate_hindi_tts(summary_final)
-        if audio_path:# and os.path.exists(audio_path):
-            # Convert audio file to base64
-            with open(audio_path, "rb") as f:
-                audio_base64 = base64.b64encode(f.read()).decode('utf-8')
-            
-            output["Audio"] = audio_base64
-
-            return output
-
-        else:
-            return jsonify({"error": "Failed to generate audio"}), 500
-    
-    elif source == "Yahoo News":
-        url = f"https://finance.yahoo.com/quote/{company}/news/"
-        headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36 Edg/120.0.0.0'}
-        response = requests.get(url, headers=headers)
-
-        if response.status_code != 200:
-            print("Failed to fetch news articles")
-            return {}
-
-        paragraphs = []
-        titles = []
-        summaries = []
-        soup = BeautifulSoup(response.content, 'html.parser')
-
-
-        for news in soup.find_all("div", class_="holder yf-1napat3"):
-            title_all = news.find_all('h3', class_="clamp yf-82qtw3")
-            summary_all = news.find_all('p', class_="clamp yf-82qtw3")
-            for title, summary in zip(title_all, summary_all):
-                title_text = title.get_text()
-                summary_text = summary.get_text()
-                paragraph = title_text + ' ' + summary_text
-                titles.append(title_text)
-                summaries.append(summary_text)
-                paragraphs.append(paragraph)
-        
-            # Analyze sentiment and prepare the output
-            for i, paragraph in enumerate(paragraphs):
-                sentiment = analyze_sentiment(paragraph)
-                if sentiment == "POSITIVE":
-                    # positive_sentiment_count += 1
-                    overall_sentiment_count += 1
-                elif sentiment == "NEGATIVE":
-                    # negative_sentiment_count += 1
-                    overall_sentiment_count -= 1
-                # else:
-                #     neutral_sentiment_count += 1
-                # top_words = 
-                sentiment_count[sentiment] += 1
-
-            article = {
-                "Title": titles[i],
-                "Summary": summaries[i],
-                "Sentiment": sentiment
-            }
-
-            all_articles.append(article)
-
-        output["Comparitive Sentiment Score"]["Sentiment Distribution"] = sentiment_count
-
-        if overall_sentiment_count>0:
-            output["Final Sentiment Analysis"] = f"{company.capitalize()}'s lastest news is mostly positive. Potential stock growth expected."
-        elif overall_sentiment_count<0:
-            output["Final Sentiment Analysis"] = f"{company.capitalize()}'s lastest news is mostly negative. Potential stock decline expected."
-        else:
-            output["Final Sentiment Analysis"] = f"{company.capitalize()}'s lastest news is mostly neutral. Stocks going to stay stagnant for some time."
-        
-        df = pd.DataFrame(all_articles)
-        text_to_summarize = " ".join([d['Title'] + " " + d['summary'] for d in article[:5]])
-        summary_final = summarize_text(text_to_summarize)
-
-        audio_path = generate_hindi_tts(summary_final)
-        if audio_path:# and os.path.exists(audio_path):
-            # Convert audio file to base64
-            with open(audio_path, "rb") as f:
-                audio_base64 = base64.b64encode(f.read()).decode('utf-8')
-            
-            output["Audio"] = audio_base64
-
-            return output
-
-        else:
-            return jsonify({"error": "Failed to generate audio"}), 500
-        # df = pd.DataFrame(all_articles)
-        # text_to_summarize = " ".join([d['Title'] + " " + d['summary'] for d in article[:5]])
-        # summary_final = summarize_text(text_to_summarize)
-
-        # audio_path = generate_hindi_tts(summary_final)
-        # if audio_path:# and os.path.exists(audio_path):
-        #     # Convert audio file to base64
-        #     with open(audio_path, "rb") as f:
-        #         audio_base64 = base64.b64encode(f.read()).decode('utf-8')
-            
-        #     output["Audio"] = audio_base64
-
-        #     return output
-
-        # else:
-        #     return jsonify({"error": "Failed to generate audio"}), 500
-    else:
-        return jsonify({"error": "Invalid source provided"}), 400 
-
-if __name__ == '__main__':
-    app.run(debug=True, port=8000)
+from flask import Flask, request, jsonify, send_file
+from bs4 import BeautifulSoup
+from newspaper import Article
+from textblob import TextBlob
+# from newsapi import NewsApiClient
+from transformers import pipeline
+import requests
+from utils import *
+import pandas as pd
+import base64
+import json
+# import nest_asyncio
+
+app = Flask(__name__)
+
+# newsapi = NewsApiClient(api_key='YOUR_NEWS_API_KEY')  # Replace with your API key
+
+# @app.route('/analyze_news', methods=['GET'])
+# def analyze_news():
+def analyze_news(company, source):
+    company = company
+    source = source
+    # company = request.args.get('company')
+    # source = request.args.get('source')
+    if not company or not source:
+        return jsonify({"error": "Please provide a company name as a query parameter"}), 400
+    
+    all_articles = []
+    output = {"Company": f"{company}", "Articles": all_articles}
+    
+    overall_sentiment_count = 0
+    sentiment_count = {"POSITIVE": 0, "NEGATIVE": 0, "NEUTRAL": 0}
+
+    if source == "NewsOrg":
+        # Fetch articles from News API
+        # response = newsapi.get_everything(q=company, page_size=5, sort_by='publishedAt', language='en')
+
+        params = {"q":"tesla","apiKey":"7396bdb0bc0a42c5b5b0c9c5945d32fa", "pagesize":10, "sortBy": "publishedAt", "language":'en'}
+        articles = requests.get(url = "https://newsapi.org/v2/everything", params= params)
+        articles = json.loads(articles.text)
+
+        # results = []
+        # sentiment_count = {"POSITIVE": 0, "NEGATIVE": 0, "NEUTRAL": 0}
+
+        # print(f">>>>>>>>>>>>>>>>>>>>>{articles}")
+        for idx, article in enumerate(articles["articles"]):
+            # print(f">>>>>>>>>>>>>>>>>>>>>{article}")
+            url = article.get("url")
+            news_article = Article(url)
+            try:
+                news_article.download()
+                news_article.parse()
+            except:
+                continue
+
+            blob = TextBlob(news_article.text)
+            polarity = blob.sentiment.polarity
+
+            if polarity > 0.3:
+                sentiment = "POSITIVE"
+                overall_sentiment_count += 1
+            elif polarity < -0.3:
+                sentiment = "NEGATIVE"
+                overall_sentiment_count -= 1
+            else:
+                sentiment = "NEUTRAL"
+                # neutral_sentiment_count += 1
+            
+            sentiment_count[sentiment] += 1
+
+            all_articles.append({
+                "Title": article.get("title"),
+                "Summary": article.get("description"),
+                "Sentiment": sentiment
+            })
+
+        output["Comparitive Sentiment Score"] = {
+            "Sentiment Distribution": sentiment_count
+        }
+
+        if overall_sentiment_count>0:
+            output["Final Sentiment Analysis"] = f"{company.capitalize()}'s lastest news is mostly positive. Potential stock growth expected."
+        elif overall_sentiment_count<0:
+            output["Final Sentiment Analysis"] = f"{company.capitalize()}'s lastest news is mostly negative. Potential stock decline expected."
+        else:
+            output["Final Sentiment Analysis"] = f"{company.capitalize()}'s lastest news is mostly neutral. Stocks going to stay stagnant for some time."
+        
+        print(output)
+        print(f"{'>'*5} Starting text summarization.")
+
+        # return jsonify({
+        #     "company": company,
+        #     "sentiment_distribution": sentiment_count,
+        #     "articles": results
+        # })
+        # df = pd.DataFrame(all_articles)
+        text_to_summarize = " ".join([d['Title'] + " " + d['Summary'] for d in all_articles[:5]])
+        summary_final = summarize_text(text_to_summarize)
+
+        audio_path = generate_hindi_tts(summary_final)
+        if audio_path:# and os.path.exists(audio_path):
+            # Convert audio file to base64
+            with open(audio_path, "rb") as f:
+                audio_base64 = base64.b64encode(f.read()).decode('utf-8')
+            
+            output["Audio"] = audio_base64
+
+            return output
+
+        else:
+            return jsonify({"error": "Failed to generate audio"}), 500
+    
+    elif source == "Yahoo News":
+        url = f"https://finance.yahoo.com/quote/{company}/news/"
+        headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36 Edg/120.0.0.0'}
+        response = requests.get(url, headers=headers)
+
+        if response.status_code != 200:
+            print("Failed to fetch news articles")
+            return {}
+
+        paragraphs = []
+        titles = []
+        summaries = []
+        soup = BeautifulSoup(response.content, 'html.parser')
+
+
+        for news in soup.find_all("div", class_="holder yf-1napat3"):
+            title_all = news.find_all('h3', class_="clamp yf-82qtw3")
+            summary_all = news.find_all('p', class_="clamp yf-82qtw3")
+            for title, summary in zip(title_all, summary_all):
+                title_text = title.get_text()
+                summary_text = summary.get_text()
+                paragraph = title_text + ' ' + summary_text
+                titles.append(title_text)
+                summaries.append(summary_text)
+                paragraphs.append(paragraph)
+        
+            # Analyze sentiment and prepare the output
+            for i, paragraph in enumerate(paragraphs):
+                sentiment = analyze_sentiment(paragraph)
+                if sentiment == "POSITIVE":
+                    # positive_sentiment_count += 1
+                    overall_sentiment_count += 1
+                elif sentiment == "NEGATIVE":
+                    # negative_sentiment_count += 1
+                    overall_sentiment_count -= 1
+                # else:
+                #     neutral_sentiment_count += 1
+                # top_words = 
+                sentiment_count[sentiment] += 1
+
+            article = {
+                "Title": titles[i],
+                "Summary": summaries[i],
+                "Sentiment": sentiment
+            }
+
+            all_articles.append(article)
+
+        output["Comparitive Sentiment Score"] = {
+            "Sentiment Distribution": sentiment_count
+        }
+        
+        if overall_sentiment_count>0:
+            output["Final Sentiment Analysis"] = f"{company.capitalize()}'s lastest news is mostly positive. Potential stock growth expected."
+        elif overall_sentiment_count<0:
+            output["Final Sentiment Analysis"] = f"{company.capitalize()}'s lastest news is mostly negative. Potential stock decline expected."
+        else:
+            output["Final Sentiment Analysis"] = f"{company.capitalize()}'s lastest news is mostly neutral. Stocks going to stay stagnant for some time."
+        
+        df = pd.DataFrame(all_articles)
+        text_to_summarize = " ".join([d['Title'] + " " + d['summary'] for d in article[:5]])
+        summary_final = summarize_text(text_to_summarize)
+
+        audio_path = generate_hindi_tts(summary_final)
+        if audio_path:# and os.path.exists(audio_path):
+            # Convert audio file to base64
+            with open(audio_path, "rb") as f:
+                audio_base64 = base64.b64encode(f.read()).decode('utf-8')
+            
+            output["Audio"] = audio_base64
+
+            return output
+
+        else:
+            return jsonify({"error": "Failed to generate audio"}), 500
+        # df = pd.DataFrame(all_articles)
+        # text_to_summarize = " ".join([d['Title'] + " " + d['summary'] for d in article[:5]])
+        # summary_final = summarize_text(text_to_summarize)
+
+        # audio_path = generate_hindi_tts(summary_final)
+        # if audio_path:# and os.path.exists(audio_path):
+        #     # Convert audio file to base64
+        #     with open(audio_path, "rb") as f:
+        #         audio_base64 = base64.b64encode(f.read()).decode('utf-8')
+            
+        #     output["Audio"] = audio_base64
+
+        #     return output
+
+        # else:
+        #     return jsonify({"error": "Failed to generate audio"}), 500
+    else:
+        return jsonify({"error": "Invalid source provided"}), 400 
+
+if __name__ == '__main__':
+    app.run(debug=True, port=8000)