Create industry_research.py

industry_research.py

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+import requests
+from bs4 import BeautifulSoup
+from transformers import pipeline
+import re
+
+# Initialize NLP model for text summarization
+summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+
+def get_company_info(url):
+    try:
+        response = requests.get(url, timeout=5)
+        soup = BeautifulSoup(response.text, 'html.parser')
+        
+        mission = soup.find('div', {'class': 'mission'}).get_text(strip=True) if soup.find('div', {'class': 'mission'}) else "Mission not found"
+        description = soup.find('meta', {'name': 'description'})['content'] if soup.find('meta', {'name': 'description'}) else "Description not found"
+        
+        summary = summarizer(description, max_length=30, min_length=10, do_sample=False)[0]['summary_text']
+        
+        industry = "Unknown"
+        if re.search(r'\b(finance|banking|insurance)\b', summary, re.I):
+            industry = "Finance"
+        elif re.search(r'\b(retail|ecommerce|shopping)\b', summary, re.I):
+            industry = "Retail"
+        elif re.search(r'\b(manufacturing|production|supply chain)\b', summary, re.I):
+            industry = "Manufacturing"
+        
+        focus_areas = []
+        if "customer" in summary:
+            focus_areas.append("customer experience")
+        if "operation" in summary or "supply chain" in summary:
+            focus_areas.append("operations")
+        
+        return {"mission": mission, "industry": industry, "focus_areas": focus_areas}
+    
+    except Exception as e:
+        print(f"Error fetching company info: {e}")
+        return {"mission": "N/A", "industry": "N/A", "focus_areas": []}