Update app2.py

app2.py

@@ -434,44 +434,6 @@ def safe_find(element, selector, class_=None, attr=None):
         return found.get(attr) if attr else found.text.strip()
     return "Not available"
 
-def search_lawyers(case_type: str, location: str) -> List[Dict[str, str]]:
-    """
-    Searches for lawyers or law firms based on case type and location using web scraping from justia.com.
-    Returns a list of dictionaries containing lawyer/firm name, website, and description.
-    """
-    base_url = "https://www.justia.com/lawyers/"
-    search_url = f"{base_url}{case_type.lower().replace(' ', '-')}-lawyer/{location.lower().replace(' ', '-')}"
-    
-    headers = {
-        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36'
-    }
-    
-    try:
-        response = requests.get(search_url, headers=headers, timeout=10)
-        response.raise_for_status()
-        soup = BeautifulSoup(response.content, 'html.parser')
-        
-        lawyers = []
-        for lawyer in soup.find_all('div', class_='lawyer-card'):
-            name = safe_find(lawyer, 'h4', class_='lawyer-name')
-            website = safe_find(lawyer, 'a', class_='lawyer-website', attr='href')
-            description = safe_find(lawyer, 'p', class_='lawyer-description')
-            
-            if name != "Not available" and website != "Not available":
-                lawyers.append({
-                    "name": name,
-                    "website": website,
-                    "description": description
-                })
-            
-            if len(lawyers) == 5:
-                break
-        
-        return lawyers
-    except requests.RequestException as e:
-        print(f"Error fetching lawyer data: {e}")
-        return []
-
 def search_web_duckduckgo(query: str, num_results: int = 3) -> List[Dict[str, str]]:
     """
     Performs a web search using the DuckDuckGo search engine.
@@ -510,45 +472,6 @@ def search_web_duckduckgo(query: str, num_results: int = 3) -> List[Dict[str, st
         print(f"Error fetching web search results: {e}")
         return []
 
-def search_similar_cases(case_type: str, location: str) -> List[Dict[str, str]]:
-    """
-    Searches for similar legal cases based on case type and location using web scraping from caselaw.findlaw.com.
-    Returns a list of dictionaries containing case title, link, and brief description.
-    """
-    base_url = "https://caselaw.findlaw.com/summary/"
-    search_query = f"{case_type.replace(' ', '-')}-{location.replace(' ', '-')}"
-    search_url = f"{base_url}{search_query}.html"
-    
-    headers = {
-        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36'
-    }
-    
-    try:
-        response = requests.get(search_url, headers=headers, timeout=10)
-        response.raise_for_status()
-        soup = BeautifulSoup(response.content, 'html.parser')
-        
-        cases = []
-        for case in soup.find_all('div', class_='case-summary'):
-            title = safe_find(case, 'h3', class_='case-name')
-            link = safe_find(case, 'a', class_='case-link', attr='href')
-            description = safe_find(case, 'p', class_='case-description')
-            
-            if title != "Not available" and link != "Not available":
-                cases.append({
-                    "title": title,
-                    "link": f"https://caselaw.findlaw.com{link}",
-                    "description": description
-                })
-            
-            if len(cases) == 5:
-                break
-        
-        return cases
-    except requests.RequestException as e:
-        print(f"Error fetching case data: {e}")
-        return []
-
 def estimate_legal_costs(case_type: str, complexity: str, state: str) -> Dict[str, Any]:
     """
     Estimates legal costs based on case type, complexity, and location.
@@ -589,9 +512,6 @@ def estimate_legal_costs(case_type: str, complexity: str, state: str) -> Dict[st
     min_total = min_rate * min_hours
     max_total = max_rate * max_hours
     
-    lawyers = search_lawyers(case_type, state)
-    similar_cases = search_similar_cases(case_type, state)
-    
     cost_breakdown = {
         "Hourly rate range": f"${min_rate:.2f} - ${max_rate:.2f}",
         "Estimated hours": f"{min_hours} - {max_hours}",
@@ -629,9 +549,7 @@ def estimate_legal_costs(case_type: str, complexity: str, state: str) -> Dict[st
         "cost_breakdown": cost_breakdown,
         "high_cost_areas": high_cost_areas,
         "cost_saving_tips": cost_saving_tips,
-        "lawyer_recommendations": lawyers,
         "finding_best_lawyer_tips": lawyer_tips,
-        "similar_cases": similar_cases,
         "web_search_results": web_search_results
     }
 
@@ -685,24 +603,6 @@ def legal_cost_estimator_ui():
         for key, value in cost_estimate["cost_breakdown"].items():
             st.write(f"**{key}:** {value}")
         
-        st.header("Recommended Lawyers/Law Firms")
-        if cost_estimate["lawyer_recommendations"]:
-            for lawyer in cost_estimate["lawyer_recommendations"]:
-                st.subheader(f"[{lawyer['name']}]({lawyer['website']})")
-                st.write(lawyer['description'])
-                st.write("---")
-        else:
-            st.write("No lawyers found for the selected criteria.")
-        
-        st.header("Similar Cases")
-        if cost_estimate["similar_cases"]:
-            for case in cost_estimate["similar_cases"]:
-                st.subheader(f"[{case['title']}]({case['link']})")
-                st.write(case["description"])
-                st.write("---")
-        else:
-            st.write("No similar cases found for the selected criteria.")
-        
         st.header("Potential High-Cost Areas")
         for area in cost_estimate["high_cost_areas"]:
             st.write(f"- {area}")